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        "repositum_id": "20.500.12708/222799",
        "title": "Statistical Error Reduction for Monte Carlo Rendering",
        "date": "2025-12-14",
        "abstract": "Denoising is an important post-processing step in physically based Monte Carlo (MC) rendering. While neural networks are widely used in practice, statistical analysis has recently become a viable alternative for denoising. In this paper, we present a general framework for statistics-based error reduction of both estimated radiance and variance. Specifically, we introduce a novel denoising approach for variance estimates, which can either improve variance-aware adaptive sampling or provide additional input for image denoising in a cascaded manner. Furthermore, we present multi-transform denoising: a general and efficient correction scheme for non-normal distributions, which typically occur in MC rendering. All these contributions combine to a robust denoising pipeline that does not require any pretraining and can run efficiently on current GPU hardware. Our results show distinct advantages over previous denoising methods, especially in the range of a few hundred samples per pixel, which is of high practical relevance. Finally, we demonstrate good convergence behavior as the number of samples increases, providing predictable results with low bias that are free of hallucinated neural artifacts. In summary, our statistics-based algorithms for adaptive sampling and denoising deliver fast, consistent, low-bias variance and radiance estimates.",
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            "path": "Publication:sakai-2025-stater",
            "url": "https://www.cg.tuwien.ac.at/research/publications/2025/sakai-2025-stater/sakai-2025-stater-Representative Image.png",
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            "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/sakai-2025-stater/sakai-2025-stater-Representative Image:thumb{{size}}.png"
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        "booktitle": "Proceedings of the SIGGRAPH Asia 2025 Conference Papers",
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        "articleno": "10.2312/hpg.20251170",
        "booktitle": "High-Performance Graphics 2025 - Symposium Papers",
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        "date_to": "2025-06-25",
        "doi": "10.2312/hpg.20251170",
        "event": "High-Performance Graphics 2025",
        "isbn": "978-3-03868-291-2",
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                "name": "Geometry Processing",
                "short_abstract": "Uses concepts from applied mathematics and computer science to design efficient algorithms for the reconstruction, analysis, manipulation, simulation and transmission of complex 3D models. Example applications are collision detection, reconstruction, compression, occlusion-aware surface handling and improved sampling conditions.",
                "abstract": "We perform research in surface reconstruction from point clouds, geometry processing and geometry representations. We use parallel processing of thousands of GPU cores to enable flash geometry operations in real-time.\nNovel data structures are evaluated to reduce the time complexity of geometric operations, such as collision detection or occlusion-aware surface processing.\nIn computational geometry, we prove concepts relating samples to curves and manifolds. We also research novel surface description paradigms and according operators, representing large point cloud data in a probabilistic way. ",
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                    "Grossfurtner-2023-paego",
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                    "gruber_horst-2019-baa",
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                    "hamzic_ademir-baa",
                    "harisch-mvs",
                    "hatzinger-2026-baa",
                    "hladky-2022-QS",
                    "hofer_kerstin-2023-sketch2model",
                    "hoffer-toth-com",
                    "holom_2025-prj",
                    "ILCIK-phdthesis",
                    "jira_marcel-2024-baa",
                    "kaipel_nikolas-2025-baa",
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                    "kazda-tmd",
                    "Kellner-2021-DA",
                    "kerbl-2022-trienc",
                    "koeppel-2016-baa",
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                    "kralicek_sebastian_baa",
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                    "leimer-2016-rpe",
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                    "marin-2022-sigdt",
                    "marin-2023-pic",
                    "marin-2024-pcp",
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                    "markovich_aleksandar-2022-maa",
                    "mayr_patrick_maa",
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                    "mazza-2012-bakk",
                    "micu_2025-prj",
                    "miklautsch_florian-baa",
                    "mueller-2024-rbt",
                    "mujadzic_iman_baa",
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                    "musialski-2013-surcgf",
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                    "negi_ashish-2023-baa",
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                    "novak_martin-2018-baa",
                    "ohrhallinger_stefan-2011-001",
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                    "ohrhallinger-2016-sgp",
                    "ohrhallinger-2021-egs",
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                    "pakkanen-2026-prj",
                    "pegrisch_2018_uvs",
                    "pichler_2022_fro",
                    "pointner_michael-2017-baa",
                    "pointner_simon-2022-maa",
                    "preiner_2012_AS",
                    "preiner_2017_phd",
                    "Preiner_2019",
                    "preiner2014clop",
                    "proelss-bdp",
                    "prohaska_julia-2023-baa",
                    "Radwan-2014-CDR",
                    "Radwan-2017-Occ",
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                    "reiher_joerg-2023-pra",
                    "reiher-joerg-2024-daa",
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                    "Reisner_2013_RSB",
                    "riegler_2020_framework",
                    "roth_vdi",
                    "rozanics-lsf",
                    "rubik_michael-2022-baa",
                    "rumpelnik_martin_2020_PRM",
                    "schaffarz_2018-pra",
                    "schartmueller_2018_baa",
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                    "Schmalstieg-1997-SMO",
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                    "slavtchev-dcd",
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                    "steinschauer-2020-baa",
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                    "Tobler-1995-AAC",
                    "Tobler-1995-BGD",
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                    "WIMMER-2006-DWN",
                    "WIMMER-2006-IP",
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                    "yoo-chaeran-2025-pr",
                    "zeilinger_matthias-2020-baa",
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                "projects": [
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                    "d4388",
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                    "EVOCATION",
                    "GameTools",
                    "Harvest4D",
                    "SCI_BIM",
                    "ShapeAcquisition",
                    "Superhumans",
                    "TERAPOINTS",
                    "WorldScale"
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                "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=Geometry",
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                "name": "Modeling",
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                "abstract": "One of the major bottlenecks in the field of computer graphics is the one of content creation, particularly the creation of high-quality 3D geometric models. These models are a demand in many industries, such as entertainment, computer-aided design, urban planning, the fabrication of physical objects, as well as research areas like medical or scientific visualizations and simulations.\n\nWhile rendering - the classical field of computer graphics - has reached very high standards in the recent two decades, the issue of the creation of appropriate digital models is a difficult problem that hardly keeps pace with current demands. Indeed, it is a complex creative process that involves both artistic and technical skills.\n\nTo make this process easier, our goal in this research area is to find new methods and algorithms that facilitate creation, representation, analysis and processing of 3D models.",
                "website": "https://www.cg.tuwien.ac.at/research/area/Modeling",
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                    "Adorjan-2015",
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                    "arikan-2013-osn",
                    "birsak-thesis",
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                    "Brandstaetter2020",
                    "brunner_lukas_2022_ssb",
                    "Caic2021",
                    "clemenz_2019_rosy_fields",
                    "DOG10",
                    "dusberger-2010-msm",
                    "eibens-2021-tsgpmg",
                    "eibensteiner-2021-pmcg",
                    "eibner-2009-gpc",
                    "Erhardt_2025_Scutes",
                    "erler_2024_ppsurf",
                    "erler_philipp-2016-femfluid-prakt",
                    "erler_philipp-2017-phd",
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                    "erler-2025-lidarscout",
                    "fan-2014-scfl",
                    "ferschin-1992-kons",
                    "fiedler-2009-php",
                    "fleiss-2015-da",
                    "GAHR-2013-AGM",
                    "gamsjaeger-2022-proc",
                    "gersthofer-2016-sosob",
                    "gervautz-1989-sol1",
                    "gervautz-1993-use",
                    "griessmair-1989-def1",
                    "groeller-1992-frac",
                    "groeller-1995-mod",
                    "Groeller-1995-SDC",
                    "Grossfurtner-2023-paego",
                    "Guerrero-2014-GRF",
                    "Guerrero-2014-TPS",
                    "guerrero-2015-lsp",
                    "HECHER-2017-HDY",
                    "hiess-1992-comb",
                    "hofer_kerstin-2023-sketch2model",
                    "honic-2020-w78",
                    "HU-2017-ETM",
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                    "Kellner-2021-DA",
                    "kendlbacher-2016",
                    "klein_2019_PGG",
                    "klein_2019_PHD",
                    "klein_2019_PMP",
                    "koenigsberger-2024-msu",
                    "komon-2025-webigeo",
                    "kouril-2015-maya2cellview",
                    "kouril-2017-sccgposter",
                    "kraehsmaier-dsc",
                    "kreuzer-2016-isf",
                    "kuehtreiber-2010-ikph",
                    "leimer_2017_rbpesc",
                    "leimer_2020-cag",
                    "leimer-2016-coan",
                    "leimer-2016-rpe",
                    "leimer-2018-sar",
                    "leimer-2023-ecd",
                    "LEOPOLD-2017-ALG",
                    "LIPP-2007-CGA",
                    "LIPP-2008-IEV",
                    "LIPP-2009-PGL",
                    "LIPP-2009-PGL2",
                    "lipp2011a",
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                    "manpreet_kainth-2012-rus",
                    "miao_kaust_2018",
                    "miao_nantech_2019",
                    "miao_nar_2020",
                    "miao_thesis_2019",
                    "mindek-xmas-card-2016",
                    "mujadzic-2023-hfp",
                    "musialski-2009-sbfr",
                    "musialski-2010-pfi",
                    "musialski-2010-tof",
                    "musialski-2012-icb",
                    "musialski-2012-sur",
                    "Musialski-2013-ipmum",
                    "musialski-2013-surcgf",
                    "NIEDERREITER-2010-SIM",
                    "Pahr2020",
                    "Parzer_2013_IIG",
                    "pegrisch_2018_uvs",
                    "pogrzebacz-2014-gggm",
                    "polzer-sog",
                    "preiner_2017_phd",
                    "PUEYO-2019-SCL",
                    "RADAX-2010-LOD",
                    "Radwan_2021_Occ",
                    "rasch-2016-imgses",
                    "recheis-2009-arr",
                    "Reichinger-12",
                    "reisinger-2023-iad",
                    "reisner-2010-ind",
                    "reisner-2011-akgeo",
                    "reitsamer-2008-tsud",
                    "Rumpelnik_Martin-2020-NPR",
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                    "grosse-2009-avp",
                    "Grossfurtner-2023-paego",
                    "grossmann-2016-baa",
                    "gruber_horst-2019-baa",
                    "Gruber2019",
                    "Guerrero_Paul_2014_EPG",
                    "guerrero-2008-dip",
                    "guerrero-2008-sli",
                    "Guerrero-2014-GRF",
                    "Guerrero-2014-TPS",
                    "guerrero-2015-lsp",
                    "gurtler-2014-ssggm",
                    "Gusenbauer_Matthias_2015_ANM",
                    "Habel_07_xms",
                    "Habel_08_SSH",
                    "Habel_09_PGT",
                    "Habel_09_PGT",
                    "Habel_2007_IAG",
                    "Habel_2007_RTT",
                    "Habel_2009_PhD",
                    "Habel_2012_PSP",
                    "Habel_LSN_2011",
                    "Habel_RAV_2010",
                    "Habel-09-RAT",
                    "Habel-09-THB",
                    "Habel-2010-EIN",
                    "hafner_christian-2013-oflow",
                    "hafner-2015-eigf",
                    "HAFNER-2015-FEM",
                    "hafner-2015-onff",
                    "halbritter-2024-m2m",
                    "hamzic_ademir-baa",
                    "hanko-2019-ani",
                    "HANN_2022_IPT",
                    "harisch-mvs",
                    "Hartl_2006_OgC",
                    "hatzinger-2026-baa",
                    "hauer_alex-2013-ba",
                    "havran-2005-egsr",
                    "havran-2005-sccg",
                    "havran-2006-tut",
                    "Hebart-2011-ColorAdjustment",
                    "HECHER-2010-3DS",
                    "hecher-2012-MH",
                    "hecher-2014-MH",
                    "HECHER-2017-HDY",
                    "heinz-2019-psi",
                    "heiss-rfr",
                    "herzberger-2021-cmake",
                    "hofer_kerstin-2023-sketch2model",
                    "hoffer-toth-com",
                    "holom_2025-prj",
                    "Holzer-06-HDR",
                    "Honic_2021",
                    "hornacek-2010-evp",
                    "horvath_imp",
                    "houska-2020-IPGM",
                    "HU-2017-ETM",
                    "huber-2025-esl",
                    "huerbe-2019-BA",
                    "HUMMEL-2001-MSE",
                    "Ilcik_2015_LAY",
                    "ilcik-2010-ps",
                    "ilcik-2011-pmous",
                    "ilcik-2013-cipmi",
                    "ilcik-2013-pmsg",
                    "ilcik-2014-cgbpmi",
                    "ilcik-2016-cmssg",
                    "ILCIK-phdthesis",
                    "ilcikova-thesis",
                    "intel2016",
                    "Ishmukhametov_Denis_2011_EIN",
                    "jafari-2023-avk",
                    "jahrmann_klemens_KFR",
                    "JAHRMANN-2013-IGR",
                    "JAHRMANN-2015-GRASS",
                    "JAHRMANN-2016-IGR",
                    "JAHRMANN-2017-RRTG",
                    "jeschke-05-AIP",
                    "jeschke-05-AIP",
                    "jeschke-05-ARI",
                    "jeschke-05-ISTAR",
                    "jeschke-09-praguetalk",
                    "jeschke-09-rendering",
                    "jeschke-09-solver",
                    "Jeschke-2002-LEM",
                    "Jeschke-2002-LEMA",
                    "Jeschke-2002-TDM",
                    "Jeschke-2002-TDMR",
                    "JESCHKE-2007-ISC",
                    "jeschke-2010-diff",
                    "jeschke-2011-est",
                    "jeschke-2011-esttalk",
                    "jeschke-2011-talkPrague",
                    "Jia-thesis",
                    "Jimenez_SSS_2015",
                    "jira_marcel-2024-baa",
                    "JORDAKIEVA-2017-VFR",
                    "KAFKA-2014-19MRD",
                    "kaipel_nikolas-2025-baa",
                    "kapferer_2021_advancing",
                    "karampekios_anastasios-prj",
                    "Kathi-2018-VRB",
                    "kazda-tmd",
                    "Keilman2022",
                    "kendlbacher-2016",
                    "kenzel_michael_2021_cuda",
                    "kerbl_2019_planet_poster",
                    "kerbl_2021_hdg",
                    "kerbl-2020-improvencoding",
                    "kerbl-2022-cuda",
                    "kerbl-2022-trienc",
                    "kimmersdorfer-2021-mzip",
                    "kimmersdorfer-2024-vcpr",
                    "klaffenboeck-2024-rva",
                    "klaffenboeck-thesis",
                    "KLAUS-2020-PCC",
                    "klein-2020-rtgi",
                    "kment_2008_cc",
                    "knecht_2013_RSM",
                    "knecht_martin_2010_DIR",
                    "knecht_martin_2012_BRDFEstimation",
                    "knecht_martin_2012_RSMR",
                    "knecht_martin_2013_ReflRefrObjsMR",
                    "knecht_martin-2011-FPSPAR",
                    "Knecht_Schwaerzler_2008_ash",
                    "knecht-2007-ao",
                    "knecht-2009-MKN",
                    "knecht-2009-TAS",
                    "knecht-2011-CBCM",
                    "knoetzl_alexander-2025-prj",
                    "koch-2019-PR",
                    "KOCH-2021-GVS",
                    "KOCH-2021-GVSDA",
                    "koenigsberger-2024-msu",
                    "koeppel-2016-baa",
                    "Koeppl-2023-DLO",
                    "koessler-2013-BA",
                    "Kogelnig_Philip-2008-OGC",
                    "kohlhas_jakob-2023-pra",
                    "Kollmann-2015-DoF",
                    "komon-2026-rso",
                    "Komon2022",
                    "konrad-2020-baa",
                    "Kovacs_2017",
                    "kovacs-2023-ttm",
                    "kozonits_marcel-2025-baa",
                    "kralicek_sebastian_baa",
                    "KREUZER-2014-DPA",
                    "KREUZER-2015-DPA",
                    "kreuzer-2016-isf",
                    "KREUZER-2017-PBF",
                    "kristmann-2022-occ",
                    "kristmann-2023-vkl",
                    "KRISTMANN-24-JPG",
                    "kristmann-thesis",
                    "Kroesl_2020_11_09",
                    "Kroesl_Katharina_2016_PPT",
                    "kroesl-2012-rtpm",
                    "kroesl-2017-LiteMaker",
                    "kroesl-2018-DC",
                    "kroesl-2018-TVS",
                    "kroesl-2019-ICthroughVR",
                    "kroesl-2019-MoD",
                    "KROESL-2020-SVI",
                    "kroesl-2020-XREye",
                    "kubicek-2024-baa",
                    "KUE11",
                    "kuehtreiber-2010-ikph",
                    "Kugler-2021",
                    "kusternig-2009-rtr",
                    "laager_florian-2013-camr",
                    "laager_florian-2014-daa",
                    "labschuetz-2010-bms",
                    "labschuetz-2011-rsr",
                    "Lan25",
                    "landauer-2022-kido",
                    "lang_jakob-2022-baa",
                    "Langer_2013_ExactGeom",
                    "lazarevic-tci",
                    "LEHNINGER-2007-SSV",
                    "leimer_2017_rbpesc",
                    "leimer_2020-cag",
                    "leimer_2020-cag",
                    "leimer_2020-cag",
                    "leimer-2013-esopc",
                    "leimer-2014-fsco",
                    "leimer-2016-coan",
                    "leimer-2016-rpe",
                    "leimer-2018-sar",
                    "leimer-2018-sar",
                    "leimer-2023-ecd",
                    "LEOPOLD-2017-ALG",
                    "leopold-2019-vox",
                    "leutschacher_2025_prj",
                    "Liang-2023-TVR",
                    "lipp_markus-2010-DAC",
                    "LIPP-2007-CGA",
                    "LIPP-2008-IEV",
                    "LIPP-2009-PGL",
                    "LIPP-2009-PGL2",
                    "LIPP-2010-PGMS",
                    "lipp-2019-rtxq3",
                    "LIPP-2020-PHD",
                    "lipp-2024-val",
                    "lipp2011a",
                    "liu-2012-tcvd",
                    "Luidolt-2020-DA",
                    "luidolt-2020-lightperceptionVR",
                    "Luksch_2007_RHR",
                    "luksch_2008_asm",
                    "luksch_2020",
                    "LUKSCH-2009-BBC",
                    "LUKSCH-2013-FLM",
                    "LUKSCH-2014-RTR",
                    "LUKSCH-2019-IGI",
                    "macho_jonas-2024-baa",
                    "MAESTRI-2012-MRW",
                    "manpreet_kainth-2012-rus",
                    "Mantler-06-landscape",
                    "MANTLER-2007-DMBBC",
                    "marek-2011-normalest",
                    "marin-2022-sig",
                    "marin-2022-sigdt",
                    "marin-2023-pic",
                    "marin-2024-dsr",
                    "marin-2024-pcp",
                    "marin-2024-pcp",
                    "marin-2024-rcf",
                    "marin-2024-sing",
                    "marin-thesis",
                    "markovich_aleksandar-2022-maa",
                    "Martinez-2015-TTB",
                    "maschek-2010-rts",
                    "matt2011",
                    "MATTAUSCH-2006-AVC",
                    "mattausch-2007-iav",
                    "MATTAUSCH-2007-OSP",
                    "mattausch-2008-CHC",
                    "mattausch-2010-tao",
                    "mattausch-2010-var",
                    "Mattausch-2010-vcr",
                    "MATTAUSCH-2012-EOV",
                    "MATTAUSCH-2012-TIS",
                    "MATTAUSCH-2013-FSBE",
                    "MATTAUSCH-2015-CHCRT",
                    "mayer_2011_cipa",
                    "MAYER-2010-MTX",
                    "Mayer-2010-VT",
                    "mayr_patrick_maa",
                    "Mayrhauser-2016-Cnc",
                    "MAYRHAUSER-2016-SCA",
                    "mazza-2012-bakk",
                    "Meier_2024_WIALT",
                    "Meier_2024_WIALT",
                    "MeierStauffer-2013-iihf",
                    "metzger-2024-smv",
                    "micu_2025-prj",
                    "miklautsch_florian-baa",
                    "MOELLINGER-2010-SD",
                    "MORARU-2014-CPD",
                    "mortezapoor-2022-photogrammabot",
                    "Mortezapoor2021ssc",
                    "Muehlbacher_2012_RRM",
                    "Muehlbacher_2012_RTR",
                    "MuehlbacherArbesser-2009-LMDLightcuts",
                    "MUEHLBERGER-2010-AFSM",
                    "mueller-2024-rbt",
                    "mujadzic_iman_baa",
                    "mujadzic-2023-hfp",
                    "murturi_PGG",
                    "musialski_2012_aachen",
                    "musialski_2012_fice",
                    "musialski_2016_sosp",
                    "musialski-2012-icb",
                    "musialski-2012-sur",
                    "Musialski-2013-ipmum",
                    "musialski-2013-prag",
                    "musialski-2013-surcgf",
                    "musialski-2015-ista",
                    "musialski-2015-pixel",
                    "musialski-2015-souos",
                    "musialski-2015-vrvis",
                    "nawrocki-2024-nsr",
                    "negi_ashish-2023-baa",
                    "Neum03CAMR",
                    "Neum04GCM",
                    "neumann-2004-aip",
                    "Neumann-2004-HOB",
                    "neumann-2004-ipm",
                    "neumann-2006-gamma",
                    "neumann-sfd",
                    "Niederreiter_2011_SM",
                    "NIEDERREITER-2010-SIM",
                    "NK_2011",
                    "novak_martin-2018-baa",
                    "Nuernberg-2011-CBO",
                    "ohrhallinger_stefan-2011-001",
                    "ohrhallinger_stefan-2012-mle",
                    "ohrhallinger_stefan-2013-c2d",
                    "ohrhallinger_stefan-2013-smi",
                    "ohrhallinger_stefan-2018-cgf",
                    "ohrhallinger_stefan-2018-pg",
                    "ohrhallinger_stefan-2022-it1",
                    "ohrhallinger_stefan-2022-tut",
                    "ohrhallinger_stefan-2023-con",
                    "ohrhallinger_stefan-2024-inv",
                    "ohrhallinger-2016-sgp",
                    "ohrhallinger-2016-sgp",
                    "ohrhallinger-2021-egs",
                    "ohrhallinger-2022-e2t",
                    "ohrhallinger-stefan-2012-the",
                    "OPPITZ-2017-3DM",
                    "oppitz-2023-ais",
                    "OPPTIZ-2018-IVRL",
                    "OTEPKA-2020-PPC",
                    "ott-rgi",
                    "Pajer-2006-tmcg",
                    "pakkanen-2026-prj",
                    "panfili-2019-VAVR",
                    "panfili-2021-myop",
                    "pangerl_2013_GPD",
                    "papantonakis-2024-rmf",
                    "parakkat-2024-ballmerge",
                    "parakkat-2024-ballmerge",
                    "PARZER-2010-STELL",
                    "PB-VRVis-2018-005",
                    "pegrisch_2018_uvs",
                    "PERNDORFER-2015-ECS",
                    "pernsteiner-2022-msh",
                    "pernsteinre_jakob_2020_eechc",
                    "Petersen_Viktoria-2024-SBO",
                    "pezenka_lukas2012-bspcga",
                    "pichler_2022_fro",
                    "pichler_2022_fro",
                    "PIRINGER-2003-HBOQ",
                    "piskernik-2010-eud",
                    "pistellato-2023-elp",
                    "PLUCH-2009-ANP",
                    "PODARAS-2017-ACRS",
                    "Podaras-praktikum-2016",
                    "pogrzebacz-2014-gggm",
                    "pogrzebacz-2023-vkl",
                    "pointner_michael-2017-baa",
                    "pointner_simon-2022-maa",
                    "POLLAK-2016-STEREO",
                    "polzer-sog",
                    "pr10",
                    "praeauer-2011-et",
                    "Prast_Stefanie_2013-CLG",
                    "preiner_2010_GIPC",
                    "preiner_2012_AS",
                    "preiner_2012_AS",
                    "Preiner_2019",
                    "PREINER-2009-GIPC",
                    "preiner11IR",
                    "preiner2014clop",
                    "Preymann-2022-pytamashii",
                    "prieler_11_patchmatch",
                    "prieler_daniel-2013-da",
                    "prieler_daniel-2013-pra",
                    "probst_kolesik_2010_pk10",
                    "probst-2023-para",
                    "proelss-bdp",
                    "prohaska_julia-2023-baa",
                    "prost-2016-molecule",
                    "PROST-2019-RTPAL",
                    "Przemyslaw_Gora_2016_UVU",
                    "PUEYO-2019-SCL",
                    "Purgathofer_2012_IVA",
                    "purgathofer-1988-simple",
                    "purgathofer-2004-asp",
                    "purgathofer-2004-comp",
                    "purgathofer-2004-neue",
                    "purgathofer-2004-real",
                    "purgathofer-2004-some",
                    "Purgathofer-2005-Ros",
                    "Purgathofer-2005-Tub",
                    "Purgathofer-2006-VRVis",
                    "Purgathofer-2009-rus",
                    "purgathofer-2010-cgtrends",
                    "Purgathofer-2014-EurasiaGr",
                    "radax-2008-ir",
                    "RADAX-2010-LOD",
                    "radax-2010-RTL",
                    "Radax2008",
                    "radits_2011_gahdr",
                    "Radwan_2021_Occ",
                    "Radwan-2014-CDR",
                    "Radwan-2017-Occ",
                    "radwan-thesis",
                    "Rainer_2017",
                    "rait_alexius-2021-baa",
                    "Rait2022",
                    "rasch_martina-2013-HDRImage",
                    "rasoulzadeh-2022-strokes2surface",
                    "rasoulzadeh-2024-strokes2surface",
                    "RAUCH-2011-TMS",
                    "recheis-2009-arr",
                    "REICHHOLF-2023-HRO",
                    "Reichinger-12",
                    "reiher_joerg-2023-pra",
                    "reiher-joerg-2024-daa",
                    "Reimerth_Saskia_2016_RGU",
                    "REINALTER_2010_VIS",
                    "reinwald-2017-baa",
                    "reisinger-2023-iad",
                    "Reisner_2013_RSB",
                    "Reisner_Irene_2013_R3D",
                    "reitsamer-2008-tsud",
                    "reznicek-2020-fpgaray",
                    "richter-2020-ani",
                    "riegler_2020_framework",
                    "roch-2007-rtt",
                    "Roch2006",
                    "Roegner_RHF_2010",
                    "ROEGNER-2014-TUI",
                    "ROEGNER-2015-IBR",
                    "ROESSLER-2012-OGLES",
                    "roth_2021_vdst",
                    "roth_vdi",
                    "roth-2023-vlk",
                    "rozanics-lsf",
                    "rubik_michael-2022-baa",
                    "Rudolf_06_IDM",
                    "rumpelnik_martin_2020_PRM",
                    "rumpelnik_martin_2020_PRM",
                    "Rumpelnik_Martin-2020-NPR",
                    "rumpelnik-2022-vlk",
                    "rumpler_2019_PPC",
                    "Rumpler-2019-PPC",
                    "RUMPLER-2021-VOGL",
                    "rumpler-2023-dcmcm",
                    "S05",
                    "SABADELLO-2006-HEG",
                    "sakai_2013",
                    "SAKAI-2015-EAPI",
                    "sakai-2021-eif",
                    "sakai-2024-asa",
                    "sakai-2025-stater",
                    "sakai-thesis",
                    "santos-2017-dacge",
                    "sari2008mt",
                    "SATZER-2008",
                    "schaffarz_2018-pra",
                    "scharl-master-thesis",
                    "schartmueller_2018_baa",
                    "schaukowitsch-2013-fls",
                    "SCHAUKOWITSCH-2015-REMOTE",
                    "SCHAUKOWITSCH-2015-STELL",
                    "schedl-2012-dof",
                    "schedl-2013-gP4",
                    "schedl-thesis",
                    "schefcik_juergen_2015_baa",
                    "Scheiblauer-2006-DA",
                    "Scheiblauer-2008-DCW",
                    "SCHEIBLAUER-2009-IDCE",
                    "scheiblauer-2011-cag",
                    "scheiblauer-2011-chnt",
                    "scheiblauer-2012-chnt",
                    "scheiblauer-2013-wscg",
                    "SCHEIBLAUER-2015-WFC",
                    "scheiblauer-thesis",
                    "scheiblauer-thesis",
                    "scherzer_2010a",
                    "Scherzer-2007-PCS",
                    "SCHERZER-2008-FSR",
                    "scherzer-2009-isvc",
                    "SCHERZER-2012-TCM",
                    "scherzer-eg2010",
                    "scherzer-thesis",
                    "scherzer2010b",
                    "scherzer2010d",
                    "scherzer2010e",
                    "scherzer2011c",
                    "scherzer2011d",
                    "schmid_andreas_2013-prj",
                    "schober_florian_2012_aspm",
                    "schoerkhuber_dominik-2016-baa",
                    "scholz_2017_bac",
                    "schrammel_sebastian_baa",
                    "schuetz_markus-2013-pra",
                    "SCHUETZ-2014-RRLP",
                    "SCHUETZ-2015-HQP",
                    "SCHUETZ-2016-POT",
                    "schuetz-2018-PPC",
                    "schuetz-2019-CLOD",
                    "schuetz-2019-LCO",
                    "SCHUETZ-2020-MPC",
                    "schuetz-2020-PPC",
                    "SCHUETZ-2021-DISS",
                    "SCHUETZ-2021-PCC",
                    "SCHUETZ-2021-PCC",
                    "SCHUETZ-2022-PCC",
                    "SCHUETZ-2023-LOD",
                    "SCHUETZ-2024-SIMLOD",
                    "schuetz-2025-splatshop",
                    "schulz-faf",
                    "schwaerzler_2018_phd",
                    "schwaerzler-2009-ass",
                    "SCHWAERZLER-2012-FAS",
                    "SCHWAERZLER-2013-FPCSS",
                    "SCHWAERZLER-2017-SBGM",
                    "schwarz-2024-m2m",
                    "Schwengerer_Mathias-2022-BGL",
                    "seidemann-bachelor-2013",
                    "seisenbacher-cyclesafely",
                    "Selig_2005_WRO",
                    "Selig-2007-VegAnim",
                    "Semmelrath-2022-IRS",
                    "seyr-2025-lbt",
                    "SIETZEN-2017-BBO",
                    "silvana_2014",
                    "sippl-2014-fss",
                    "slajcho_levente-baa",
                    "slavtchev-dcd",
                    "Spechtenhauser_Florian_2013_SIU",
                    "spelitz_stefan-2012-CDTFMR",
                    "sperl-2013-BA",
                    "Sperl-praktikum-2016",
                    "spielmann-2006-ner",
                    "spitaler-2015-pbi",
                    "spitaler-afr-2011",
                    "SSMW09",
                    "stappen_SteFAS",
                    "stappen-2019-vrs",
                    "starnberger-cli",
                    "stauss-2025-gfd",
                    "steiner_2016_isad",
                    "steiner_2016_isad",
                    "STEINER-2006-SVC",
                    "steiner-2013-BS",
                    "steiner-2014-da",
                    "steinheber-2024-oto",
                    "steinkellner-2022-dll",
                    "steinkellner-2024-dll",
                    "STEINLECHNER-2019-APS",
                    "steinschauer-2020-baa",
                    "steinschorn-2023-p2m",
                    "steinschorn-2023-p2m",
                    "steinschorn-2025-par",
                    "steinschorn-2025-parameter",
                    "Stingl_2011_RHS",
                    "STINGL-2009-RHS",
                    "Stoll2020",
                    "STREICHER-2022-DSLS",
                    "Streicher-bachelor-2013",
                    "STU-SR-25",
                    "sturl_andreas-caflm",
                    "STURN-2013-LGI",
                    "STURN-2013-LSI",
                    "Sturn-2013-ST",
                    "sturn-thesis",
                    "stutter_david-2012-kft",
                    "subtil-2006-disp",
                    "SUNDSTEDT-2008-ASF",
                    "sundstedt-2013-vag",
                    "szabo_2013_ssrr",
                    "Tanzmeister_2011_I3D",
                    "tatzgern-2020-sst",
                    "Thann_2019_01",
                    "themmer-2018-fbx",
                    "thurner-2019-ani",
                    "TK_2011",
                    "tobler02_amrmg",
                    "tobler02_mbpls",
                    "tobler97_hsdasr",
                    "TOMASCHITZ-2020-EP",
                    "TOROMANOFF-2005-ASA",
                    "TR 193-02-2019-4",
                    "TR-186-2-02-04",
                    "TR-186-2-05-05",
                    "TR-186-2-06-02",
                    "TR-186-2-07-01",
                    "TR-186-2-07-09",
                    "TR-186-2-08-09",
                    "TR-186-2-12-01",
                    "Tragust-2012-GMHPC",
                    "Tragust-2014-master-thesis",
                    "traxler-1997-TRA",
                    "trenkwalder-2008-landscape",
                    "trenkwalder-2013-ma",
                    "trenovatz-lpd",
                    "trumpf_stefan_2012",
                    "Tucek_2018",
                    "Tucek_Tom-2016-aai",
                    "tumpach_barbara-pra",
                    "ulschmid-2024-reo",
                    "ulschmid-2025-apc",
                    "ulschmid-phdthesis",
                    "unterguggenberger-2010-cmph",
                    "unterguggenberger-2020-fmvr",
                    "unterguggenberger-2021-msh",
                    "unterguggenberger-2022-vulkan",
                    "unterguggenberger-2023-vaw",
                    "unterguggenberger-2024-fropo",
                    "unterguggenberger-phdthesis",
                    "VASCO-2019-EVR",
                    "vasiljevic-gpc",
                    "VASILJEVS-2018-PMPL",
                    "vis-foa",
                    "VOELKL-2013-RTRS",
                    "voglreiter-2023-tro",
                    "Voglsam_2012_RTR",
                    "Voglsam_2013_RRT",
                    "wagner-2024-par",
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        ],
        "articleno": "8",
        "doi": "10.1111/cgf.70214",
        "issn": "1467-8659",
        "journal": "Computer Graphics Forum",
        "note": "https://github.com/m-schuetz/Splatshop",
        "number": "8",
        "open_access": "yes",
        "pages": "12",
        "publisher": "WILEY",
        "volume": "44",
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                "id": "Rendering",
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                "name": "Rendering",
                "short_abstract": "In this area, we concentrate on algorithms that synthesize images to depict 3D models or scenes, often by simulating or approximating the physics of light.",
                "abstract": "The rendering of 3D models and scenes has been a major, long-standing challenge in computer graphics that can be divided into two major subfields. <em>Real-time rendering</em> refers to the real-time synthesis of images on commodity graphics hardware to facilitate interaction for visualizations or computer games. The field of <em>physically based rendering</em> focuses on algorithms that accurately simulate the physics of light for use in the movie industry, architectural visualizations, etc. Compared to real-time rendering methods, these algorithms generally require a considerable amount of time (i.e., hours up to days) to generate acceptable results.\n\nIn the context of real-time rendering, our work revolves around the development of algorithms to achieve higher visual quality. Furthermore, we work on techniques to accelerate physically based rendering algorithms to make them viable for interactive rendering. Another aspect of our research work revolves around tools that facilitate the work of content creators.",
                "website": "https://www.cg.tuwien.ac.at/research/area/Rendering",
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                "short_abstract": "<p class=\"text-align-justify\">Point clouds are a quintessential 3D geometry representation format, and often the first model obtained from reconstructive efforts, such as LIDAR scans. IVILPC aims for fast, authentic, interactive, and high-quality processing of such point-based data sets. Our project explores high-performance software rendering routines for various point-based primitives, such as point sprites, gaussian splats, surfels, and particle systems. Beyond conventional use cases, point cloud rendering also forms a key component of point-based machine learning methods and novel-view synthesis, where performance is paramount. We will exploit the flexibility and processing power of cutting-edge GPU architecture features to formulate novel, high-performance rendering approaches. The envisioned solutions will be applicable to unstructured point clouds for instant rendering of billions of points. Our research targets minimally-invasive compression, culling methods, and level-of-detail techniques for point-based rendering to deliver high performance and quality on-demand. We explore GPU-accelerated editing of point clouds, as well as common display issues on next-generation display devices. IVILPC lays the foundation for interaction with large point clouds in conventional and immersive environments. Its goal is an efficient data knowledge transfer from sensor to user, with a wide range of use cases to image-based rendering, virtual reality (VR) technology, architecture, the geospatial industry, and cultural heritage.</p>\r\n",
                "abstract": "<p class=\"text-align-justify\">Point clouds are a quintessential 3D geometry representation format, and often the first model obtained from reconstructive efforts, such as LIDAR scans. IVILPC aims for fast, authentic, interactive, and high-quality processing of such point-based data sets. Our project explores high-performance software rendering routines for various point-based primitives, such as point sprites, gaussian splats, surfels, and particle systems. Beyond conventional use cases, point cloud rendering also forms a key component of point-based machine learning methods and novel-view synthesis, where performance is paramount. We will exploit the flexibility and processing power of cutting-edge GPU architecture features to formulate novel, high-performance rendering approaches. The envisioned solutions will be applicable to unstructured point clouds for instant rendering of billions of points. Our research targets minimally-invasive compression, culling methods, and level-of-detail techniques for point-based rendering to deliver high performance and quality on-demand. We explore GPU-accelerated editing of point clouds, as well as common display issues on next-generation display devices. IVILPC lays the foundation for interaction with large point clouds in conventional and immersive environments. Its goal is an efficient data knowledge transfer from sensor to user, with a wide range of use cases to image-based rendering, virtual reality (VR) technology, architecture, the geospatial industry, and cultural heritage.</p>\r\n",
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    {
        "id": "celarek-2025-d3g",
        "type_id": "journalpaper_notalk",
        "tu_id": null,
        "repositum_id": "20.500.12708/219918",
        "title": "Does 3D Gaussian Splatting Need Accurate Volumetric Rendering?",
        "date": "2025-05",
        "abstract": "Since its introduction, 3D Gaussian Splatting (3DGS) has become an important reference method for learning 3D representations of a captured scene, allowing real-time novel-view synthesis with high visual quality and fast training times. Neural Radiance Fields (NeRFs), which preceded 3DGS, are based on a principled ray-marching approach for volumetric rendering. In contrast, while sharing a similar image formation model with NeRF, 3DGS uses a hybrid rendering solution that builds on the strengths of volume rendering and primitive rasterization. A crucial benefit of 3DGS is its performance, achieved through a set of approximations, in many cases with respect to volumetric rendering theory. A naturally arising question is whether replacing these approximations with more principled volumetric rendering solutions can improve the quality of 3DGS. In this paper, we present an in-depth analysis of the various approximations and assumptions used by the original 3DGS solution. We demonstrate that, while more accurate volumetric rendering can help for low numbers of primitives, the power of efficient optimization and the large number of Gaussians allows 3DGS to outperform volumetric rendering despite its approximations.",
        "authors_et_al": false,
        "substitute": null,
        "main_image": null,
        "sync_repositum_override": null,
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        "authors": [
            {
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                "website": "https://www.cg.tuwien.ac.at/staff/AdamCelarek",
                "firstname": "Adam",
                "lastname": "Celarek",
                "searchname": "Adam Celarek",
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                "short_abstract": "In this area, we concentrate on algorithms that synthesize images to depict 3D models or scenes, often by simulating or approximating the physics of light.",
                "abstract": "The rendering of 3D models and scenes has been a major, long-standing challenge in computer graphics that can be divided into two major subfields. <em>Real-time rendering</em> refers to the real-time synthesis of images on commodity graphics hardware to facilitate interaction for visualizations or computer games. The field of <em>physically based rendering</em> focuses on algorithms that accurately simulate the physics of light for use in the movie industry, architectural visualizations, etc. Compared to real-time rendering methods, these algorithms generally require a considerable amount of time (i.e., hours up to days) to generate acceptable results.\n\nIn the context of real-time rendering, our work revolves around the development of algorithms to achieve higher visual quality. Furthermore, we work on techniques to accelerate physically based rendering algorithms to make them viable for interactive rendering. Another aspect of our research work revolves around tools that facilitate the work of content creators.",
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                "name": "Advanced Computational Design",
                "name_de": "Advanced Computational Design",
                "short_title": "ACD",
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                "abstract": "<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Research question:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> The main research question addressed by the SFB </span><strong style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">“</strong><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Advanced Computational Design</em><strong style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">”</strong><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> is how to advance design tools and processes through multi- and interdisciplinary basic research in the areas of digital architecture, integrated building design, computer graphics and virtual reality, discrete and applied geometry, and computational mechanics.&nbsp;</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Wider research context:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> Architecture, Engineering and Construction (AEC) shapes our built environment, exerting substantial environmental, cultural and economic influence on society. However, it is among the least digitized industries, still caught in silo-thinking and sequential planning processes. The Information and Communication Technology field, on the other hand, is highly innovative, creating digital design tools that are well-founded in basic research, but often lack relevant domain knowledge, thus hardly meeting designers’ needs.</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Innovation:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> We will connect architecture, computer science, mathematics and engineering in order to develop advanced computational design tools able to incorporate implicit and explicit design knowledge. This unique combination of scientific disciplines and collaborative research with strong theoretical foundations aims to bring radical innovation in computational design by allowing immediate feedback already in early design phases and by expanding solution spaces by computing design variants that cannot be found by current methods.</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Approach:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> The proposed research is structured in three areas: Design Methodology (A1), Visual and Haptic Design Interaction (A2) and Form Finding (A3). A1 uses ontologies to describe AEC design semantics, coupled to a novel digital mixed-reality sketchbook, and an innovative implicit modeling approach based on retrieving and enhancing 2D images and 3D point clouds through transfer learning. A1 also acts as a platform for integrating and evaluating the computational tools and methods developed in A2 and A3. These areas will investigate research questions in computational design involving algorithmic solutions. A2 investigates real-time global-illumination and optimization algorithms for lighting design, as well as a new method for large-scale haptic interactions in virtual reality based on a mobile robotic platform. In A3, form finding will be explored regarding geometric, mechanical and material constraints, in particular: paneling of complex shapes by patches of certain surface classes while optimizing the number of molds; algorithms for finding new transformable quad-surfaces; mechanical models for an efficient simulation of bio-composite material systems. Furthermore, new ways of form-finding will be explored experimentally, which will allow for validating the developed algorithmic approaches and reconsidering model assumptions and constraints.</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Added value:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> The fundamental computational tools and methods to be developed will be applicable in AEC and other fields of the creative industries. </span></p>",
                "start_date": "2020-03-01",
                "end_date": "2028-02-29",
                "leader_id": 193,
                "logo": null,
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                "abstract": "<p>Photorealistic rendering, which is essential for many applications, involves evaluating complicated integrals. These integrals are predominantly approximated with a Monte Carlo method called path tracing. Unfortunately, this method generally requires many samples and therefore long computation times for noiseless images. Recently, so-called path-guiding techniques have been introduced, which gather knowledge about the integrands to facilitate sampling. In this project, we seek to identify and exploit untapped potential in path guiding to provide significant improvements in this active line of research. The two principal problems in path guiding are i) representing gathered knowledge and ii) sampling using that representation. We strive toward the ideal of optimal path guiding (considering practical constraints), which leads us to our main research questions: Q1. How can we represent path-guiding knowledge optimally? Q2. How can we sample optimally using that representation? We pursue two novel key ideas for possible answers: For Q1, we cast the problem of representing knowledge as a mathematical optimization problem, enabling us to exploit the knowledge and methods from the field of optimization. For Q2, we introduce “scalable product importance sampling”: adapting sampling accuracy as required to economize on resources. We see great potential for synergies between the two ideas and for them to open up new lines of investigation, thereby inspiring many follow-up works.</p>",
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        "booktitle": "Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - GRAPP",
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        "doi": "10.5220/0013193900003912",
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                "abstract": "<p class=\"text-align-justify\">Point clouds are a quintessential 3D geometry representation format, and often the first model obtained from reconstructive efforts, such as LIDAR scans. IVILPC aims for fast, authentic, interactive, and high-quality processing of such point-based data sets. Our project explores high-performance software rendering routines for various point-based primitives, such as point sprites, gaussian splats, surfels, and particle systems. Beyond conventional use cases, point cloud rendering also forms a key component of point-based machine learning methods and novel-view synthesis, where performance is paramount. We will exploit the flexibility and processing power of cutting-edge GPU architecture features to formulate novel, high-performance rendering approaches. The envisioned solutions will be applicable to unstructured point clouds for instant rendering of billions of points. Our research targets minimally-invasive compression, culling methods, and level-of-detail techniques for point-based rendering to deliver high performance and quality on-demand. We explore GPU-accelerated editing of point clouds, as well as common display issues on next-generation display devices. IVILPC lays the foundation for interaction with large point clouds in conventional and immersive environments. Its goal is an efficient data knowledge transfer from sensor to user, with a wide range of use cases to image-based rendering, virtual reality (VR) technology, architecture, the geospatial industry, and cultural heritage.</p>\r\n",
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        "articleno": "68",
        "booktitle": "SA '24: SIGGRAPH Asia 2024 Conference Papers",
        "date_from": "2024-12-03",
        "date_to": "2024-12-06",
        "doi": "10.1145/3680528.3687591",
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                "website": "https://www.cg.tuwien.ac.at/staff/HiroyukiSakai",
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                "abstract": "The rendering of 3D models and scenes has been a major, long-standing challenge in computer graphics that can be divided into two major subfields. <em>Real-time rendering</em> refers to the real-time synthesis of images on commodity graphics hardware to facilitate interaction for visualizations or computer games. The field of <em>physically based rendering</em> focuses on algorithms that accurately simulate the physics of light for use in the movie industry, architectural visualizations, etc. Compared to real-time rendering methods, these algorithms generally require a considerable amount of time (i.e., hours up to days) to generate acceptable results.\n\nIn the context of real-time rendering, our work revolves around the development of algorithms to achieve higher visual quality. Furthermore, we work on techniques to accelerate physically based rendering algorithms to make them viable for interactive rendering. Another aspect of our research work revolves around tools that facilitate the work of content creators.",
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                    "Skydome",
                    "Superhumans",
                    "TERAPOINTS",
                    "Treelumination",
                    "UrbanViz",
                    "Visual Computing",
                    "Wohnen 4.0",
                    "WorldScale"
                ],
                "research_areas": [
                    "Fabrication",
                    "Geometry",
                    "Modeling",
                    "Perception",
                    "Rendering",
                    "VR"
                ],
                "publications": [
                    "2011scherzerE",
                    "2018_Mautner_Praktikum",
                    "ABG25",
                    "ADORJAN-2013-ASE",
                    "Adorjan-2015",
                    "agyemang-2021-bsdfproxyppg",
                    "aichner-2016-sadf",
                    "albillos-2013-MA",
                    "Ams25",
                    "antes-2026-maa",
                    "arikan-2013-osn",
                    "arikan-2014-pcvis",
                    "arikan-2015-dmrt",
                    "arikan-thesis",
                    "Arleo-2019-vis",
                    "Artusi-2001-Col",
                    "Artusi-2003-CCG",
                    "Artusi-2003-Col",
                    "Artusi-2003-Del",
                    "ASCHL-2022-OTV",
                    "AUER-2010-PPT3D",
                    "ausserer-2026-baa",
                    "Auzinger_2012_AAA",
                    "Auzinger_2012_GeigerCam",
                    "Auzinger_2013_AnaVis",
                    "Auzinger_2013_NSAA",
                    "Auzinger_2013_SAR",
                    "Auzinger_2014_DCGI",
                    "Auzinger_2014_UJA",
                    "Auzinger-2014-WiGr",
                    "Auzinger-2015-IST",
                    "auzinger-2015-phd",
                    "auzinger-2015-utokyo",
                    "auzinger-dissertation",
                    "auzinger-thesis",
                    "bagar_2008",
                    "BAGAR-2010-LPB",
                    "bagar2010",
                    "balvan-rsf",
                    "BARSUKOV-2010-NEPC",
                    "bauchinger-2007-mre",
                    "Bauer_Dominik_2018_VR",
                    "BAUER-2009-AFSS",
                    "BAUER-2024-PWG",
                    "BAUER-2025-PCW",
                    "beck-2007-melk",
                    "beham_2009.2009-EVS",
                    "BEHAM-2014-RCR",
                    "beleznai_csaba-2025-pr",
                    "belic_dejan_pr",
                    "Bernhard_Matthias_2014_VAC",
                    "bernhard-2010-gph",
                    "bernhard-2011-bmtf",
                    "bernhard-2011-bmtf",
                    "bernhard-2011-maicg",
                    "bernhard-2014-EFD",
                    "bernhard-2014-GTOM",
                    "bhagvat-09-frusta",
                    "birsak-2013-sta",
                    "birsak-2014-agtb",
                    "birsak-2017-dpe",
                    "birsak-2017-dpe",
                    "Birsak-Michael-2012",
                    "birsak-thesis",
                    "birsak-thesis",
                    "Birsak2018-SA",
                    "Bittner-2001-Vis",
                    "Bittner-2003-Vis",
                    "Bittner-2004-CHC",
                    "bittner-2005-egsr",
                    "bittner-2005-egsr",
                    "BITTNER-2009-AGVS",
                    "BITTNER-2009-GEFOC",
                    "bittner-2011-scc",
                    "bodzenta-2026-baa",
                    "Bogner-2008-MTh",
                    "boindl_2013",
                    "BOKSANSKY-2019-RTS",
                    "BORZA-2012-Stellarium",
                    "brandmair-2024-bind",
                    "brandmair-2024-rust",
                    "brandmair-2025-rub",
                    "Brandorff_06_PON",
                    "Brandorff_06_SKIN",
                    "brenner_simon-2013-ba",
                    "brugger-2020-tdp",
                    "brugger-2020-tsdpbr",
                    "brugger-2020-tsdpbr",
                    "brunner_lukas_2022_ssb",
                    "Brusi-2001-Opt",
                    "CADIK-2006-IAQ",
                    "CADIK-2008-EHD",
                    "Calatrava_2013_GPGPUSOA",
                    "cardoso-2021-cost",
                    "cardoso-2022-rtpercept",
                    "cardoso-2024-r-c",
                    "cardoso-thesis",
                    "cech-2023-taa",
                    "celarek_adam-2012-rrmro",
                    "celarek_adam-2019-qelta",
                    "celarek_adam-2019-qelta",
                    "CELAREK-2017-QCL",
                    "CELAREK-2017-QCL",
                    "celarek-2022-gmcn",
                    "celarek-2022-gmcn",
                    "celarek-2022-gmcn",
                    "celarek-thesis",
                    "CHARALAMBOS-2007-HLOD",
                    "charpenay-2013-PR",
                    "charpenay-2014-sgn",
                    "Che-2025",
                    "CHIU-2008-PEN",
                    "clemenz_2019_rosy_fields",
                    "CORNEL-2010-VIS",
                    "CORNEL-2014-AFRS",
                    "CORNEL-2017-FRS",
                    "CORNEL-2019-IVF",
                    "csecsy-fcd",
                    "darovskikh_leonid-2025-prj",
                    "dataset",
                    "dataset-cat",
                    "dataset-chair",
                    "dataset-dragon",
                    "dataset-duck",
                    "dataset-ferris-wheel",
                    "dataset-penguin",
                    "dataset-strauss",
                    "dataset-tramway",
                    "dataset-viennacity",
                    "daubner-2007-HDR",
                    "deak-a3",
                    "deix-2008-rcp",
                    "dellmour-2013-grs",
                    "depner_dennis-2020-baa",
                    "depner_dennis-2022-maa",
                    "Derflinger-2024-RTTW",
                    "Devlin-2002-STA",
                    "dodik-2017-pcbpt",
                    "Doerflinger-2016-CDV",
                    "Dogangonul-2013-sparkGL",
                    "druml-2010-PFL",
                    "Druml-2013-3DrC",
                    "dusberger-2010-msm",
                    "ecormier-nocca-2025-sls",
                    "eibens-2021-tsgpmg",
                    "eibensteiner-2016-pmg",
                    "eibensteiner-2021-pmcg",
                    "eickmeyer-2027-tcd",
                    "EISEMANN-2011-RTS",
                    "EISEMANN-2012-ERT",
                    "EISEMANN-2013-ERT",
                    "elagrod_ahmed-baa",
                    "Erhardt_2025_Scutes",
                    "Erhardt_Seirlehner-2024-TamashiiIntegration",
                    "erler_2024_ppsurf",
                    "erler_2024_ppsurf",
                    "erler_2024_ppsurf",
                    "erler_2024_ppsurf",
                    "erler_philipp-2016-femfluid-prakt",
                    "erler_philipp-2017-phd",
                    "erler_philipp-2017-phd",
                    "erler_philipp-2017-phd",
                    "ERLER-2017-HVR",
                    "erler-2020-p2s",
                    "erler-2020-p2s",
                    "erler-2020-p2s",
                    "erler-2025-lidarscout",
                    "ERNST-2010-IOOC",
                    "ernst-2013-fif",
                    "ESBERGER-2016-AHO",
                    "ESCHNER-2019-GDT",
                    "fan-2014-scfl",
                    "fan-2014-scfl",
                    "Fauster-06-st",
                    "Felberbauer_2012_AG",
                    "fiedler-2009-php",
                    "fiedler-2022-vlk",
                    "fink-2012-cg1",
                    "fink-2013-cag",
                    "fink-2013-gvp",
                    "Fischer-2018-sssvr",
                    "fitzinger_andreas-daa",
                    "fizimayer-2007-art",
                    "fleiss-2015-da",
                    "forsythe-2016-ccm",
                    "FRAISS-2017-PCU",
                    "fraiss-2019-rtx",
                    "FRAISS-2022-CGMM",
                    "fraissl_niklas-2023-pra",
                    "freude_2020_rs",
                    "Freude_MSc",
                    "FREUDE-2015-phd",
                    "freude-2023-prh",
                    "freude-2023-prh",
                    "freude-2023-sem",
                    "freude-2023-sem",
                    "freude-2025-iso",
                    "freude-2025-iso",
                    "freude-2025-ras",
                    "Fritz-2012",
                    "fruehstueck-2008-gpu",
                    "fuchs_peter-2024-baa",
                    "fuericht_2025_prj",
                    "fuericht_elias-2025-prj",
                    "Fuericht-2026-FPD",
                    "Fuerst_2014_BSc",
                    "Fuerst-2009-dfr",
                    "FUERST-2013-RTWS",
                    "Fuerst-Bachelor-2013",
                    "G_P_06_RFS",
                    "GAHR-2013-AGM",
                    "gamper_2008_or",
                    "GAMPER-2018-OSG",
                    "gamsjaeger-2022-proc",
                    "gangl-2018-cgbase",
                    "GANTNER-2018-ARO",
                    "GANTNER-2019-CRS",
                    "GEBHART-2008-AGV",
                    "gersthofer_2017_COP",
                    "gersthofer-2016-sosob",
                    "geyer_lukas_2015_prj",
                    "geyer_lukas-2014-bat",
                    "geyer-2018-apbf",
                    "giefing_juergen-2013-ba",
                    "GIEGL-2006-QVS",
                    "GIEGL-2007-FVS",
                    "GIEGL-2007-QV1",
                    "GIEGL-2007-UNP",
                    "gilmutdinov-2022-aomlbwug",
                    "glanz-2017-pbrcomparison",
                    "goel-2024-rdr",
                    "goetz_johann-cagmfp",
                    "Goldmann_2011_GPU",
                    "gramegna-rfr",
                    "Grasberger_08_VV",
                    "Grasberger_2008_ISR",
                    "grasberger_2009_CSB",
                    "GRELAUD-2009-EPA",
                    "GRIESHOFER-2018-GOS",
                    "Grisenti-2026-HSHPG",
                    "grosse-2009-avp",
                    "Grossfurtner-2023-paego",
                    "grossmann-2016-baa",
                    "gruber_horst-2019-baa",
                    "Gruber2019",
                    "Guerrero_Paul_2014_EPG",
                    "guerrero-2008-dip",
                    "guerrero-2008-sli",
                    "Guerrero-2014-GRF",
                    "Guerrero-2014-TPS",
                    "guerrero-2015-lsp",
                    "gurtler-2014-ssggm",
                    "Gusenbauer_Matthias_2015_ANM",
                    "Habel_07_xms",
                    "Habel_08_SSH",
                    "Habel_09_PGT",
                    "Habel_09_PGT",
                    "Habel_2007_IAG",
                    "Habel_2007_RTT",
                    "Habel_2009_PhD",
                    "Habel_2012_PSP",
                    "Habel_LSN_2011",
                    "Habel_RAV_2010",
                    "Habel-09-RAT",
                    "Habel-09-THB",
                    "Habel-2010-EIN",
                    "hafner_christian-2013-oflow",
                    "hafner-2015-eigf",
                    "HAFNER-2015-FEM",
                    "hafner-2015-onff",
                    "halbritter-2024-m2m",
                    "hamzic_ademir-baa",
                    "hanko-2019-ani",
                    "HANN_2022_IPT",
                    "harisch-mvs",
                    "Hartl_2006_OgC",
                    "hatzinger-2026-baa",
                    "hauer_alex-2013-ba",
                    "havran-2005-egsr",
                    "havran-2005-sccg",
                    "havran-2006-tut",
                    "Hebart-2011-ColorAdjustment",
                    "HECHER-2010-3DS",
                    "hecher-2012-MH",
                    "hecher-2014-MH",
                    "HECHER-2017-HDY",
                    "heinz-2019-psi",
                    "heiss-rfr",
                    "herzberger-2021-cmake",
                    "hofer_kerstin-2023-sketch2model",
                    "hoffer-toth-com",
                    "holom_2025-prj",
                    "Holzer-06-HDR",
                    "Honic_2021",
                    "hornacek-2010-evp",
                    "horvath_imp",
                    "houska-2020-IPGM",
                    "HU-2017-ETM",
                    "huber-2025-esl",
                    "huerbe-2019-BA",
                    "HUMMEL-2001-MSE",
                    "Ilcik_2015_LAY",
                    "ilcik-2010-ps",
                    "ilcik-2011-pmous",
                    "ilcik-2013-cipmi",
                    "ilcik-2013-pmsg",
                    "ilcik-2014-cgbpmi",
                    "ilcik-2016-cmssg",
                    "ILCIK-phdthesis",
                    "ilcikova-thesis",
                    "intel2016",
                    "Ishmukhametov_Denis_2011_EIN",
                    "jafari-2023-avk",
                    "jahrmann_klemens_KFR",
                    "JAHRMANN-2013-IGR",
                    "JAHRMANN-2015-GRASS",
                    "JAHRMANN-2016-IGR",
                    "JAHRMANN-2017-RRTG",
                    "jeschke-05-AIP",
                    "jeschke-05-AIP",
                    "jeschke-05-ARI",
                    "jeschke-05-ISTAR",
                    "jeschke-09-praguetalk",
                    "jeschke-09-rendering",
                    "jeschke-09-solver",
                    "Jeschke-2002-LEM",
                    "Jeschke-2002-LEMA",
                    "Jeschke-2002-TDM",
                    "Jeschke-2002-TDMR",
                    "JESCHKE-2007-ISC",
                    "jeschke-2010-diff",
                    "jeschke-2011-est",
                    "jeschke-2011-esttalk",
                    "jeschke-2011-talkPrague",
                    "Jia-thesis",
                    "Jimenez_SSS_2015",
                    "jira_marcel-2024-baa",
                    "JORDAKIEVA-2017-VFR",
                    "KAFKA-2014-19MRD",
                    "kaipel_nikolas-2025-baa",
                    "kapferer_2021_advancing",
                    "karampekios_anastasios-prj",
                    "Kathi-2018-VRB",
                    "kazda-tmd",
                    "Keilman2022",
                    "kendlbacher-2016",
                    "kenzel_michael_2021_cuda",
                    "kerbl_2019_planet_poster",
                    "kerbl_2021_hdg",
                    "kerbl-2020-improvencoding",
                    "kerbl-2022-cuda",
                    "kerbl-2022-trienc",
                    "kimmersdorfer-2021-mzip",
                    "kimmersdorfer-2024-vcpr",
                    "klaffenboeck-2024-rva",
                    "klaffenboeck-thesis",
                    "KLAUS-2020-PCC",
                    "klein-2020-rtgi",
                    "kment_2008_cc",
                    "knecht_2013_RSM",
                    "knecht_martin_2010_DIR",
                    "knecht_martin_2012_BRDFEstimation",
                    "knecht_martin_2012_RSMR",
                    "knecht_martin_2013_ReflRefrObjsMR",
                    "knecht_martin-2011-FPSPAR",
                    "Knecht_Schwaerzler_2008_ash",
                    "knecht-2007-ao",
                    "knecht-2009-MKN",
                    "knecht-2009-TAS",
                    "knecht-2011-CBCM",
                    "knoetzl_alexander-2025-prj",
                    "koch-2019-PR",
                    "KOCH-2021-GVS",
                    "KOCH-2021-GVSDA",
                    "koenigsberger-2024-msu",
                    "koeppel-2016-baa",
                    "Koeppl-2023-DLO",
                    "koessler-2013-BA",
                    "Kogelnig_Philip-2008-OGC",
                    "kohlhas_jakob-2023-pra",
                    "Kollmann-2015-DoF",
                    "komon-2026-rso",
                    "Komon2022",
                    "konrad-2020-baa",
                    "Kovacs_2017",
                    "kovacs-2023-ttm",
                    "kozonits_marcel-2025-baa",
                    "kralicek_sebastian_baa",
                    "KREUZER-2014-DPA",
                    "KREUZER-2015-DPA",
                    "kreuzer-2016-isf",
                    "KREUZER-2017-PBF",
                    "kristmann-2022-occ",
                    "kristmann-2023-vkl",
                    "KRISTMANN-24-JPG",
                    "kristmann-thesis",
                    "Kroesl_2020_11_09",
                    "Kroesl_Katharina_2016_PPT",
                    "kroesl-2012-rtpm",
                    "kroesl-2017-LiteMaker",
                    "kroesl-2018-DC",
                    "kroesl-2018-TVS",
                    "kroesl-2019-ICthroughVR",
                    "kroesl-2019-MoD",
                    "KROESL-2020-SVI",
                    "kroesl-2020-XREye",
                    "kubicek-2024-baa",
                    "KUE11",
                    "kuehtreiber-2010-ikph",
                    "Kugler-2021",
                    "kusternig-2009-rtr",
                    "laager_florian-2013-camr",
                    "laager_florian-2014-daa",
                    "labschuetz-2010-bms",
                    "labschuetz-2011-rsr",
                    "Lan25",
                    "landauer-2022-kido",
                    "lang_jakob-2022-baa",
                    "Langer_2013_ExactGeom",
                    "lazarevic-tci",
                    "LEHNINGER-2007-SSV",
                    "leimer_2017_rbpesc",
                    "leimer_2020-cag",
                    "leimer_2020-cag",
                    "leimer_2020-cag",
                    "leimer-2013-esopc",
                    "leimer-2014-fsco",
                    "leimer-2016-coan",
                    "leimer-2016-rpe",
                    "leimer-2018-sar",
                    "leimer-2018-sar",
                    "leimer-2023-ecd",
                    "LEOPOLD-2017-ALG",
                    "leopold-2019-vox",
                    "leutschacher_2025_prj",
                    "Liang-2023-TVR",
                    "lipp_markus-2010-DAC",
                    "LIPP-2007-CGA",
                    "LIPP-2008-IEV",
                    "LIPP-2009-PGL",
                    "LIPP-2009-PGL2",
                    "LIPP-2010-PGMS",
                    "lipp-2019-rtxq3",
                    "LIPP-2020-PHD",
                    "lipp-2024-val",
                    "lipp2011a",
                    "liu-2012-tcvd",
                    "Luidolt-2020-DA",
                    "luidolt-2020-lightperceptionVR",
                    "Luksch_2007_RHR",
                    "luksch_2008_asm",
                    "luksch_2020",
                    "LUKSCH-2009-BBC",
                    "LUKSCH-2013-FLM",
                    "LUKSCH-2014-RTR",
                    "LUKSCH-2019-IGI",
                    "macho_jonas-2024-baa",
                    "MAESTRI-2012-MRW",
                    "manpreet_kainth-2012-rus",
                    "Mantler-06-landscape",
                    "MANTLER-2007-DMBBC",
                    "marek-2011-normalest",
                    "marin-2022-sig",
                    "marin-2022-sigdt",
                    "marin-2023-pic",
                    "marin-2024-dsr",
                    "marin-2024-pcp",
                    "marin-2024-pcp",
                    "marin-2024-rcf",
                    "marin-2024-sing",
                    "marin-thesis",
                    "markovich_aleksandar-2022-maa",
                    "Martinez-2015-TTB",
                    "maschek-2010-rts",
                    "matt2011",
                    "MATTAUSCH-2006-AVC",
                    "mattausch-2007-iav",
                    "MATTAUSCH-2007-OSP",
                    "mattausch-2008-CHC",
                    "mattausch-2010-tao",
                    "mattausch-2010-var",
                    "Mattausch-2010-vcr",
                    "MATTAUSCH-2012-EOV",
                    "MATTAUSCH-2012-TIS",
                    "MATTAUSCH-2013-FSBE",
                    "MATTAUSCH-2015-CHCRT",
                    "mayer_2011_cipa",
                    "MAYER-2010-MTX",
                    "Mayer-2010-VT",
                    "mayr_patrick_maa",
                    "Mayrhauser-2016-Cnc",
                    "MAYRHAUSER-2016-SCA",
                    "mazza-2012-bakk",
                    "Meier_2024_WIALT",
                    "Meier_2024_WIALT",
                    "MeierStauffer-2013-iihf",
                    "metzger-2024-smv",
                    "micu_2025-prj",
                    "miklautsch_florian-baa",
                    "MOELLINGER-2010-SD",
                    "MORARU-2014-CPD",
                    "mortezapoor-2022-photogrammabot",
                    "Mortezapoor2021ssc",
                    "Muehlbacher_2012_RRM",
                    "Muehlbacher_2012_RTR",
                    "MuehlbacherArbesser-2009-LMDLightcuts",
                    "MUEHLBERGER-2010-AFSM",
                    "mueller-2024-rbt",
                    "mujadzic_iman_baa",
                    "mujadzic-2023-hfp",
                    "murturi_PGG",
                    "musialski_2012_aachen",
                    "musialski_2012_fice",
                    "musialski_2016_sosp",
                    "musialski-2012-icb",
                    "musialski-2012-sur",
                    "Musialski-2013-ipmum",
                    "musialski-2013-prag",
                    "musialski-2013-surcgf",
                    "musialski-2015-ista",
                    "musialski-2015-pixel",
                    "musialski-2015-souos",
                    "musialski-2015-vrvis",
                    "nawrocki-2024-nsr",
                    "negi_ashish-2023-baa",
                    "Neum03CAMR",
                    "Neum04GCM",
                    "neumann-2004-aip",
                    "Neumann-2004-HOB",
                    "neumann-2004-ipm",
                    "neumann-2006-gamma",
                    "neumann-sfd",
                    "Niederreiter_2011_SM",
                    "NIEDERREITER-2010-SIM",
                    "NK_2011",
                    "novak_martin-2018-baa",
                    "Nuernberg-2011-CBO",
                    "ohrhallinger_stefan-2011-001",
                    "ohrhallinger_stefan-2012-mle",
                    "ohrhallinger_stefan-2013-c2d",
                    "ohrhallinger_stefan-2013-smi",
                    "ohrhallinger_stefan-2018-cgf",
                    "ohrhallinger_stefan-2018-pg",
                    "ohrhallinger_stefan-2022-it1",
                    "ohrhallinger_stefan-2022-tut",
                    "ohrhallinger_stefan-2023-con",
                    "ohrhallinger_stefan-2024-inv",
                    "ohrhallinger-2016-sgp",
                    "ohrhallinger-2016-sgp",
                    "ohrhallinger-2021-egs",
                    "ohrhallinger-2022-e2t",
                    "ohrhallinger-stefan-2012-the",
                    "OPPITZ-2017-3DM",
                    "oppitz-2023-ais",
                    "OPPTIZ-2018-IVRL",
                    "OTEPKA-2020-PPC",
                    "ott-rgi",
                    "Pajer-2006-tmcg",
                    "pakkanen-2026-prj",
                    "panfili-2019-VAVR",
                    "panfili-2021-myop",
                    "pangerl_2013_GPD",
                    "papantonakis-2024-rmf",
                    "parakkat-2024-ballmerge",
                    "parakkat-2024-ballmerge",
                    "PARZER-2010-STELL",
                    "PB-VRVis-2018-005",
                    "pegrisch_2018_uvs",
                    "PERNDORFER-2015-ECS",
                    "pernsteiner-2022-msh",
                    "pernsteinre_jakob_2020_eechc",
                    "Petersen_Viktoria-2024-SBO",
                    "pezenka_lukas2012-bspcga",
                    "pichler_2022_fro",
                    "pichler_2022_fro",
                    "PIRINGER-2003-HBOQ",
                    "piskernik-2010-eud",
                    "pistellato-2023-elp",
                    "PLUCH-2009-ANP",
                    "PODARAS-2017-ACRS",
                    "Podaras-praktikum-2016",
                    "pogrzebacz-2014-gggm",
                    "pogrzebacz-2023-vkl",
                    "pointner_michael-2017-baa",
                    "pointner_simon-2022-maa",
                    "POLLAK-2016-STEREO",
                    "polzer-sog",
                    "pr10",
                    "praeauer-2011-et",
                    "Prast_Stefanie_2013-CLG",
                    "preiner_2010_GIPC",
                    "preiner_2012_AS",
                    "preiner_2012_AS",
                    "Preiner_2019",
                    "PREINER-2009-GIPC",
                    "preiner11IR",
                    "preiner2014clop",
                    "Preymann-2022-pytamashii",
                    "prieler_11_patchmatch",
                    "prieler_daniel-2013-da",
                    "prieler_daniel-2013-pra",
                    "probst_kolesik_2010_pk10",
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        "title": "Re:Draw - Context Aware Translation as a Controllable Method for Artistic Production",
        "date": "2024-08",
        "abstract": "We introduce context-aware translation, a novel method that combines the benefits of inpainting and image-to-image translation, respecting simultaneously the original input and contextual relevance – where existing methods fall short. By doing so, our method opens new avenues for the controllable use of AI within artistic creation, from animation to digital art.\nAs an use case, we apply our method to redraw any hand-drawn animated character eyes based on any design specifications – eyes serve as a focal point that captures viewer attention and conveys a range of emotions; however, the labor-intensive na-\nture of traditional animation often leads to compromises in the complexity and consistency of eye design. Furthermore, we remove the need for production data for training and introduce a new character recognition method that surpasses existing work\nby not requiring fine-tuning to specific productions.\nThis proposed use case could help maintain consistency throughout production and unlock bolder and\nmore detailed design choices without the production cost drawbacks. A user study shows contextaware translation is preferred over existing work 95.16% of the time.",
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                "website": "https://www.cg.tuwien.ac.at/staff/Joao-AfonsoCardoso",
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        "doi": "10.24963/ijcai.2024/842",
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                    "PROST-2017-RTL",
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                    "Przemyslaw_Gora_2016_UVU",
                    "purgathofer-2004-real",
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                    "SCHUETZ-2021-PCC",
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                    "sippl_tobias_2017_WPC",
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                    "sorger-2015-litevis",
                    "SSMW09",
                    "stappen_SteFAS",
                    "stappen-2019-vrs",
                    "steinkellner-2024-dll",
                    "STEINLECHNER-2019-APS",
                    "STEINLECHNER-2019-ICT",
                    "STREICHER-2022-DSLS",
                    "STU-SR-25",
                    "Sturn-2013-ST",
                    "Szabo-2018-DA",
                    "tatzgern-2020-sst",
                    "Thann_2019_01",
                    "themmer-2018-fbx",
                    "tobler97_hsdasr",
                    "TR 193-02-2019-4",
                    "TR 193-2-2019-4",
                    "TR-186-2-02-04",
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            "level-of-detail",
            "Large Scenes"
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                "id": "d9275",
                "workgroup_id": "vis",
                "drupal_id": 9275,
                "drupal_path": "/research/projects/IVILPC",
                "name": "Instant Visualization and Interaction for Large Point Clouds",
                "name_de": null,
                "short_title": "IVILPC",
                "website": null,
                "status": "active",
                "short_abstract": "<p class=\"text-align-justify\">Point clouds are a quintessential 3D geometry representation format, and often the first model obtained from reconstructive efforts, such as LIDAR scans. IVILPC aims for fast, authentic, interactive, and high-quality processing of such point-based data sets. Our project explores high-performance software rendering routines for various point-based primitives, such as point sprites, gaussian splats, surfels, and particle systems. Beyond conventional use cases, point cloud rendering also forms a key component of point-based machine learning methods and novel-view synthesis, where performance is paramount. We will exploit the flexibility and processing power of cutting-edge GPU architecture features to formulate novel, high-performance rendering approaches. The envisioned solutions will be applicable to unstructured point clouds for instant rendering of billions of points. Our research targets minimally-invasive compression, culling methods, and level-of-detail techniques for point-based rendering to deliver high performance and quality on-demand. We explore GPU-accelerated editing of point clouds, as well as common display issues on next-generation display devices. IVILPC lays the foundation for interaction with large point clouds in conventional and immersive environments. Its goal is an efficient data knowledge transfer from sensor to user, with a wide range of use cases to image-based rendering, virtual reality (VR) technology, architecture, the geospatial industry, and cultural heritage.</p>\r\n",
                "abstract": "<p class=\"text-align-justify\">Point clouds are a quintessential 3D geometry representation format, and often the first model obtained from reconstructive efforts, such as LIDAR scans. IVILPC aims for fast, authentic, interactive, and high-quality processing of such point-based data sets. Our project explores high-performance software rendering routines for various point-based primitives, such as point sprites, gaussian splats, surfels, and particle systems. Beyond conventional use cases, point cloud rendering also forms a key component of point-based machine learning methods and novel-view synthesis, where performance is paramount. We will exploit the flexibility and processing power of cutting-edge GPU architecture features to formulate novel, high-performance rendering approaches. The envisioned solutions will be applicable to unstructured point clouds for instant rendering of billions of points. Our research targets minimally-invasive compression, culling methods, and level-of-detail techniques for point-based rendering to deliver high performance and quality on-demand. We explore GPU-accelerated editing of point clouds, as well as common display issues on next-generation display devices. IVILPC lays the foundation for interaction with large point clouds in conventional and immersive environments. Its goal is an efficient data knowledge transfer from sensor to user, with a wide range of use cases to image-based rendering, virtual reality (VR) technology, architecture, the geospatial industry, and cultural heritage.</p>\r\n",
                "start_date": "2023-07-02",
                "end_date": "2026-07-01",
                "leader_id": 166,
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                "funding_organisations": [],
                "research_areas": [
                    "Geometry",
                    "Rendering",
                    "VR"
                ],
                "publications": [
                    "erler-2025-lidarscout",
                    "goel-2024-rdr",
                    "kerbl-2024-ah3",
                    "papantonakis-2024-rmf",
                    "SCHUETZ-2024-SIMLOD",
                    "schuetz-2025-splatshop",
                    "ulschmid-2024-reo",
                    "ulschmid-2025-apc",
                    "unterguggenberger-2024-fropo",
                    "voglreiter-2023-tro"
                ],
                "url": "https://www.cg.tuwien.ac.at/research/projects/IVILPC",
                "__class": "Project"
            }
        ],
        "url": "https://www.cg.tuwien.ac.at/research/publications/2024/kerbl-2024-ah3/",
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    {
        "id": "koenigsberger-2024-msu",
        "type_id": "journalpaper_notalk",
        "tu_id": null,
        "repositum_id": "20.500.12708/206831",
        "title": "Micromechanics stiffness upscaling of plant fiber-reinforced composites",
        "date": "2024-07-15",
        "abstract": "Fiber-reinforced green composites made from natural plant fibers are an increasingly popular sustainable alternative to conventional high-performance composite materials. Given the variety of natural fibers themselves, and the even larger variety of possible composites with specific fiber dosage, fiber orientation distribution, fiber length distribution, and fiber–matrix bond characteristics, micromechanics-based modeling is essential for characterizing the macroscopic response of these composites. Herein, an analytical multiscale micromechanics model for elastic homogenization is developed, capable of capturing the variety. The model features (i) a nanoscopic representation of the natural fibers to predict the fiber stiffness from the universal stiffness of the fiber constituents, mainly cellulose, (ii) a spring-interface model to quantify the compliance of the fiber–matrix bond, and (iii) the ability to model any (and any combination of) orientation distribution and aspect ratio distribution. Validation is performed by comparing the predicted stiffness to experimental results for as many as 73 composites available in the literature. Extensive sensitivity analyses quantify the composite stiffening upon increasing fiber volume fraction, fiber alignment, fiber length, and fiber–matrix interface stiffness, respectively.",
        "authors_et_al": false,
        "substitute": null,
        "main_image": null,
        "sync_repositum_override": "projects",
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        "authors": [
            {
                "id": 5423,
                "drupal_id": null,
                "orcid_id": null,
                "website": null,
                "firstname": "Markus",
                "lastname": "Königsberger",
                "searchname": "Markus Königsberger",
                "is_teacher": false,
                "name": "Markus Königsberger",
                "publications": [
                    "koenigsberger-2024-msu"
                ],
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                "project_leader": [],
                "publication_field": [],
                "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=5423",
                "__class": "Author"
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            {
                "id": 5424,
                "drupal_id": null,
                "orcid_id": null,
                "website": null,
                "firstname": "Valentin",
                "lastname": "Senk",
                "searchname": "Valentin Senk",
                "is_teacher": false,
                "name": "Valentin Senk",
                "publications": [
                    "koenigsberger-2024-msu"
                ],
                "publications_first": [],
                "project_leader": [],
                "publication_field": [],
                "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=5424",
                "__class": "Author"
            },
            {
                "id": 5425,
                "drupal_id": null,
                "orcid_id": "0000-0002-6441-8698",
                "website": null,
                "firstname": "Markus",
                "lastname": "Lukacevic",
                "searchname": "Markus Lukacevic",
                "is_teacher": false,
                "name": "Markus Lukacevic",
                "publications": [
                    "koenigsberger-2024-msu"
                ],
                "publications_first": [],
                "project_leader": [],
                "publication_field": [],
                "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=5425",
                "__class": "Author"
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                "drupal_id": 49,
                "orcid_id": "0000-0002-9370-2663",
                "website": "https://www.cg.tuwien.ac.at/staff/MichaelWimmer",
                "firstname": "Michael",
                "lastname": "Wimmer",
                "searchname": "Michael Wimmer",
                "is_teacher": true,
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                    "CHARALAMBOS-2007-HLOD",
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                    "CORNEL-2019-IVF",
                    "dataset-viennacity",
                    "ecormier-nocca-2025-sls",
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                    "EISEMANN-2012-ERT",
                    "EISEMANN-2013-ERT",
                    "erler_2024_ppsurf",
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                    "klaffenboeck-2024-rva",
                    "knecht_martin_2010_DIR",
                    "knecht_martin_2012_BRDFEstimation",
                    "knecht_martin_2012_RSMR",
                    "knecht_martin_2013_ReflRefrObjsMR",
                    "knecht_martin-2011-FPSPAR",
                    "knecht-2011-CBCM",
                    "KOCH-2021-GVS",
                    "koenigsberger-2024-msu",
                    "kovacs-2023-ttm",
                    "KREUZER-2015-DPA",
                    "Kroesl_2020_11_09",
                    "kroesl-2017-LiteMaker",
                    "kroesl-2018-TVS",
                    "kroesl-2019-ICthroughVR",
                    "kroesl-2020-XREye",
                    "leimer_2017_rbpesc",
                    "leimer-2016-rpe",
                    "LIPP-2008-IEV",
                    "LIPP-2009-PGL",
                    "LIPP-2009-PGL2",
                    "LIPP-2010-PGMS",
                    "lipp-2024-val",
                    "lipp2011a",
                    "luidolt-2020-lightperceptionVR",
                    "luksch_2020",
                    "LUKSCH-2013-FLM",
                    "LUKSCH-2014-RTR",
                    "LUKSCH-2019-IGI",
                    "MANTLER-2007-DMBBC",
                    "marin-2022-sig",
                    "marin-2022-sigdt",
                    "marin-2023-pic",
                    "marin-2024-dsr",
                    "marin-2024-pcp",
                    "marin-2024-rcf",
                    "marin-2024-sing",
                    "matt2011",
                    "MATTAUSCH-2006-AVC",
                    "MATTAUSCH-2007-OSP",
                    "mattausch-2008-CHC",
                    "mattausch-2010-tao",
                    "MATTAUSCH-2012-EOV",
                    "MATTAUSCH-2012-TIS",
                    "MATTAUSCH-2013-FSBE",
                    "MATTAUSCH-2015-CHCRT",
                    "murturi_PGG",
                    "musialski_2016_sosp",
                    "musialski-2012-icb",
                    "musialski-2012-sur",
                    "Musialski-2013-ipmum",
                    "musialski-2013-surcgf",
                    "musialski-2015-souos",
                    "ohrhallinger_stefan-2013-smi",
                    "ohrhallinger_stefan-2018-cgf",
                    "ohrhallinger_stefan-2018-pg",
                    "ohrhallinger-2016-sgp",
                    "OTEPKA-2020-PPC",
                    "panfili-2021-myop",
                    "pistellato-2023-elp",
                    "preiner_2010_GIPC",
                    "preiner_2012_AS",
                    "Preiner_2019",
                    "preiner2014clop",
                    "PUEYO-2019-SCL",
                    "PURGATHOFER-2006-IHB",
                    "Radwan_2021_Occ",
                    "Radwan-2014-CDR",
                    "Radwan-2017-Occ",
                    "rasoulzadeh-2022-strokes2surface",
                    "rasoulzadeh-2023-ani",
                    "rasoulzadeh-2024-strokes2surface",
                    "reisinger-2023-iad",
                    "ROEGNER-2015-IBR",
                    "roth_vdi",
                    "sakai-2021-eif",
                    "sakai-2024-asa",
                    "sakai-2025-stater",
                    "santos-2017-dacge",
                    "schedl-2012-dof",
                    "schedl-2013-gP4",
                    "SCHEIBLAUER-2009-IDCE",
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                    "Scherzer-2007-PCS",
                    "SCHERZER-2008-FSR",
                    "scherzer-2009-isvc",
                    "SCHERZER-2012-TCM",
                    "scherzer-eg2010",
                    "scherzer2011c",
                    "scherzer2011d",
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                    "schuetz-2019-CLOD",
                    "schuetz-2019-LCO",
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                    "SCHUETZ-2020-MPC",
                    "schuetz-2020-PPC",
                    "SCHUETZ-2021-PCC",
                    "SCHUETZ-2022-PCC",
                    "SCHUETZ-2023-LOD",
                    "SCHUETZ-2024-SIMLOD",
                    "schuetz-2025-splatshop",
                    "SCHWAERZLER-2012-FAS",
                    "SCHWAERZLER-2013-FPCSS",
                    "SCHWAERZLER-2017-SBGM",
                    "SSMW09",
                    "stappen_SteFAS",
                    "steiner_2016_isad",
                    "STEINLECHNER-2019-APS",
                    "STURN-2013-LGI",
                    "STURN-2013-LSI",
                    "SUNDSTEDT-2008-ASF",
                    "sundstedt-2013-vag",
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                "abstract": "<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Research question:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> The main research question addressed by the SFB </span><strong style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">“</strong><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Advanced Computational Design</em><strong style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">”</strong><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> is how to advance design tools and processes through multi- and interdisciplinary basic research in the areas of digital architecture, integrated building design, computer graphics and virtual reality, discrete and applied geometry, and computational mechanics.&nbsp;</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Wider research context:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> Architecture, Engineering and Construction (AEC) shapes our built environment, exerting substantial environmental, cultural and economic influence on society. However, it is among the least digitized industries, still caught in silo-thinking and sequential planning processes. The Information and Communication Technology field, on the other hand, is highly innovative, creating digital design tools that are well-founded in basic research, but often lack relevant domain knowledge, thus hardly meeting designers’ needs.</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Innovation:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> We will connect architecture, computer science, mathematics and engineering in order to develop advanced computational design tools able to incorporate implicit and explicit design knowledge. This unique combination of scientific disciplines and collaborative research with strong theoretical foundations aims to bring radical innovation in computational design by allowing immediate feedback already in early design phases and by expanding solution spaces by computing design variants that cannot be found by current methods.</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Approach:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> The proposed research is structured in three areas: Design Methodology (A1), Visual and Haptic Design Interaction (A2) and Form Finding (A3). A1 uses ontologies to describe AEC design semantics, coupled to a novel digital mixed-reality sketchbook, and an innovative implicit modeling approach based on retrieving and enhancing 2D images and 3D point clouds through transfer learning. A1 also acts as a platform for integrating and evaluating the computational tools and methods developed in A2 and A3. These areas will investigate research questions in computational design involving algorithmic solutions. A2 investigates real-time global-illumination and optimization algorithms for lighting design, as well as a new method for large-scale haptic interactions in virtual reality based on a mobile robotic platform. In A3, form finding will be explored regarding geometric, mechanical and material constraints, in particular: paneling of complex shapes by patches of certain surface classes while optimizing the number of molds; algorithms for finding new transformable quad-surfaces; mechanical models for an efficient simulation of bio-composite material systems. Furthermore, new ways of form-finding will be explored experimentally, which will allow for validating the developed algorithmic approaches and reconsidering model assumptions and constraints.</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Added value:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> The fundamental computational tools and methods to be developed will be applicable in AEC and other fields of the creative industries. </span></p>",
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        "doi": "10.1145/3662180",
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                "short_abstract": "In this area, we concentrate on algorithms that synthesize images to depict 3D models or scenes, often by simulating or approximating the physics of light.",
                "abstract": "The rendering of 3D models and scenes has been a major, long-standing challenge in computer graphics that can be divided into two major subfields. <em>Real-time rendering</em> refers to the real-time synthesis of images on commodity graphics hardware to facilitate interaction for visualizations or computer games. The field of <em>physically based rendering</em> focuses on algorithms that accurately simulate the physics of light for use in the movie industry, architectural visualizations, etc. Compared to real-time rendering methods, these algorithms generally require a considerable amount of time (i.e., hours up to days) to generate acceptable results.\n\nIn the context of real-time rendering, our work revolves around the development of algorithms to achieve higher visual quality. Furthermore, we work on techniques to accelerate physically based rendering algorithms to make them viable for interactive rendering. Another aspect of our research work revolves around tools that facilitate the work of content creators.",
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                "name": "Advanced Computational Design",
                "name_de": "Advanced Computational Design",
                "short_title": "ACD",
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                "abstract": "<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Research question:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> The main research question addressed by the SFB </span><strong style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">“</strong><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Advanced Computational Design</em><strong style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">”</strong><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> is how to advance design tools and processes through multi- and interdisciplinary basic research in the areas of digital architecture, integrated building design, computer graphics and virtual reality, discrete and applied geometry, and computational mechanics.&nbsp;</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Wider research context:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> Architecture, Engineering and Construction (AEC) shapes our built environment, exerting substantial environmental, cultural and economic influence on society. However, it is among the least digitized industries, still caught in silo-thinking and sequential planning processes. The Information and Communication Technology field, on the other hand, is highly innovative, creating digital design tools that are well-founded in basic research, but often lack relevant domain knowledge, thus hardly meeting designers’ needs.</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Innovation:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> We will connect architecture, computer science, mathematics and engineering in order to develop advanced computational design tools able to incorporate implicit and explicit design knowledge. This unique combination of scientific disciplines and collaborative research with strong theoretical foundations aims to bring radical innovation in computational design by allowing immediate feedback already in early design phases and by expanding solution spaces by computing design variants that cannot be found by current methods.</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Approach:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> The proposed research is structured in three areas: Design Methodology (A1), Visual and Haptic Design Interaction (A2) and Form Finding (A3). A1 uses ontologies to describe AEC design semantics, coupled to a novel digital mixed-reality sketchbook, and an innovative implicit modeling approach based on retrieving and enhancing 2D images and 3D point clouds through transfer learning. A1 also acts as a platform for integrating and evaluating the computational tools and methods developed in A2 and A3. These areas will investigate research questions in computational design involving algorithmic solutions. A2 investigates real-time global-illumination and optimization algorithms for lighting design, as well as a new method for large-scale haptic interactions in virtual reality based on a mobile robotic platform. In A3, form finding will be explored regarding geometric, mechanical and material constraints, in particular: paneling of complex shapes by patches of certain surface classes while optimizing the number of molds; algorithms for finding new transformable quad-surfaces; mechanical models for an efficient simulation of bio-composite material systems. Furthermore, new ways of form-finding will be explored experimentally, which will allow for validating the developed algorithmic approaches and reconsidering model assumptions and constraints.</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Added value:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> The fundamental computational tools and methods to be developed will be applicable in AEC and other fields of the creative industries. </span></p>",
                "start_date": "2020-03-01",
                "end_date": "2028-02-29",
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        "title": "SimLOD: Simultaneous LOD Generation and Rendering for Point Clouds",
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        "abstract": "LOD construction is typically implemented as a preprocessing step that requires users to wait before they are able to view the results in real time. We propose an incremental LOD generation approach for point clouds that allows us to simultaneously load points from disk, update an octree-based level-of-detail representation, and render the intermediate results in real time while additional points are still being loaded from disk. LOD construction and rendering are both implemented in CUDA and share the GPU’s processing power, but each incremental update is lightweight enough to leave enough time to maintain real-time frame rates. Our approach is able to stream points from an SSD and update the octree on the GPU at rates of up to 580 million points per second (~9.3GB/s) on an RTX 4090 and a PCIe 5.0 SSD. Depending on the data set, our approach spends an average of about 1 to 2 ms to incrementally insert 1 million points into the octree, allowing us to insert several million points per frame into the LOD structure and render the intermediate results within the same frame.",
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        "issn": "2577-6193",
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        "number": "1",
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        "pages": "20",
        "publisher": "Association for Computing Machinery (ACM)",
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                "short_abstract": "<p class=\"text-align-justify\">Point clouds are a quintessential 3D geometry representation format, and often the first model obtained from reconstructive efforts, such as LIDAR scans. IVILPC aims for fast, authentic, interactive, and high-quality processing of such point-based data sets. Our project explores high-performance software rendering routines for various point-based primitives, such as point sprites, gaussian splats, surfels, and particle systems. Beyond conventional use cases, point cloud rendering also forms a key component of point-based machine learning methods and novel-view synthesis, where performance is paramount. We will exploit the flexibility and processing power of cutting-edge GPU architecture features to formulate novel, high-performance rendering approaches. The envisioned solutions will be applicable to unstructured point clouds for instant rendering of billions of points. Our research targets minimally-invasive compression, culling methods, and level-of-detail techniques for point-based rendering to deliver high performance and quality on-demand. We explore GPU-accelerated editing of point clouds, as well as common display issues on next-generation display devices. IVILPC lays the foundation for interaction with large point clouds in conventional and immersive environments. Its goal is an efficient data knowledge transfer from sensor to user, with a wide range of use cases to image-based rendering, virtual reality (VR) technology, architecture, the geospatial industry, and cultural heritage.</p>\r\n",
                "abstract": "<p class=\"text-align-justify\">Point clouds are a quintessential 3D geometry representation format, and often the first model obtained from reconstructive efforts, such as LIDAR scans. IVILPC aims for fast, authentic, interactive, and high-quality processing of such point-based data sets. Our project explores high-performance software rendering routines for various point-based primitives, such as point sprites, gaussian splats, surfels, and particle systems. Beyond conventional use cases, point cloud rendering also forms a key component of point-based machine learning methods and novel-view synthesis, where performance is paramount. We will exploit the flexibility and processing power of cutting-edge GPU architecture features to formulate novel, high-performance rendering approaches. The envisioned solutions will be applicable to unstructured point clouds for instant rendering of billions of points. Our research targets minimally-invasive compression, culling methods, and level-of-detail techniques for point-based rendering to deliver high performance and quality on-demand. We explore GPU-accelerated editing of point clouds, as well as common display issues on next-generation display devices. IVILPC lays the foundation for interaction with large point clouds in conventional and immersive environments. Its goal is an efficient data knowledge transfer from sensor to user, with a wide range of use cases to image-based rendering, virtual reality (VR) technology, architecture, the geospatial industry, and cultural heritage.</p>\r\n",
                "start_date": "2023-07-02",
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                    "SCHUETZ-2024-SIMLOD",
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                "name": "Efficient workflow transforming large 3D point clouds to Building Information Models with user-assisted automatization",
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        "title": "Strokes2Surface: Recovering Curve Networks From 4D Architectural Design Sketches",
        "date": "2024-05",
        "abstract": "We present Strokes2Surface, an offline geometry reconstruction pipeline that recovers well-connected curve networks from imprecise 4D sketches to bridge concept design and digital modeling stages in architectural design. The input to our pipeline consists of 3D strokes' polyline vertices and their timestamps as the 4th dimension, along with additional metadata recorded throughout sketching. Inspired by architectural sketching practices, our pipeline combines a classifier and two clustering models to achieve its goal. First, with a set of extracted hand-engineered features from the sketch, the classifier recognizes the type of individual strokes between those depicting boundaries (Shape strokes) and those depicting enclosed areas (Scribble strokes). Next, the two clustering models parse strokes of each type into distinct groups, each representing an individual edge or face of the intended architectural object. Curve networks are then formed through topology recovery of consolidated Shape clusters and surfaced using Scribble clusters guiding the cycle discovery. Our evaluation is threefold: We confirm the usability of the Strokes2Surface pipeline in architectural design use cases via a user study, we validate our choice of features via statistical analysis and ablation studies on our collected dataset, and we compare our outputs against a range of reconstructions computed using alternative methods.",
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                "firstname": "Iva",
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        ],
        "articleno": "e15054",
        "doi": "10.1111/cgf.15054",
        "issn": "1467-8659",
        "journal": "Computer Graphics Forum",
        "number": "2",
        "pages": "16",
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        "publisher": "WILEY",
        "volume": "43",
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        "keywords": [
            "CCS Concepts",
            "Computer graphics",
            "Computing methodologies → Artificial intelligence",
            "Machine learning"
        ],
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            {
                "id": "d4314",
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                "name": "Advanced Computational Design",
                "name_de": "Advanced Computational Design",
                "short_title": "ACD",
                "website": "http://acd.tuwien.ac.at",
                "status": "active",
                "short_abstract": null,
                "abstract": "<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Research question:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> The main research question addressed by the SFB </span><strong style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">“</strong><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Advanced Computational Design</em><strong style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">”</strong><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> is how to advance design tools and processes through multi- and interdisciplinary basic research in the areas of digital architecture, integrated building design, computer graphics and virtual reality, discrete and applied geometry, and computational mechanics.&nbsp;</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Wider research context:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> Architecture, Engineering and Construction (AEC) shapes our built environment, exerting substantial environmental, cultural and economic influence on society. However, it is among the least digitized industries, still caught in silo-thinking and sequential planning processes. The Information and Communication Technology field, on the other hand, is highly innovative, creating digital design tools that are well-founded in basic research, but often lack relevant domain knowledge, thus hardly meeting designers’ needs.</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Innovation:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> We will connect architecture, computer science, mathematics and engineering in order to develop advanced computational design tools able to incorporate implicit and explicit design knowledge. This unique combination of scientific disciplines and collaborative research with strong theoretical foundations aims to bring radical innovation in computational design by allowing immediate feedback already in early design phases and by expanding solution spaces by computing design variants that cannot be found by current methods.</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Approach:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> The proposed research is structured in three areas: Design Methodology (A1), Visual and Haptic Design Interaction (A2) and Form Finding (A3). A1 uses ontologies to describe AEC design semantics, coupled to a novel digital mixed-reality sketchbook, and an innovative implicit modeling approach based on retrieving and enhancing 2D images and 3D point clouds through transfer learning. A1 also acts as a platform for integrating and evaluating the computational tools and methods developed in A2 and A3. These areas will investigate research questions in computational design involving algorithmic solutions. A2 investigates real-time global-illumination and optimization algorithms for lighting design, as well as a new method for large-scale haptic interactions in virtual reality based on a mobile robotic platform. In A3, form finding will be explored regarding geometric, mechanical and material constraints, in particular: paneling of complex shapes by patches of certain surface classes while optimizing the number of molds; algorithms for finding new transformable quad-surfaces; mechanical models for an efficient simulation of bio-composite material systems. Furthermore, new ways of form-finding will be explored experimentally, which will allow for validating the developed algorithmic approaches and reconsidering model assumptions and constraints.</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Added value:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> The fundamental computational tools and methods to be developed will be applicable in AEC and other fields of the creative industries. </span></p>",
                "start_date": "2020-03-01",
                "end_date": "2028-02-29",
                "leader_id": 193,
                "logo": null,
                "funding_organisations": [],
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                    "Erhardt_Seirlehner-2024-TamashiiIntegration",
                    "freude-2023-prh",
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                    "Fuericht-2026-FPD",
                    "Grisenti-2026-HSHPG",
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                    "Koeppl-2023-DLO",
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                    "lipp-2024-val",
                    "Meier_2024_WIALT",
                    "mortezapoor-2022-photogrammabot",
                    "Mortezapoor2021ssc",
                    "Preymann-2022-pytamashii",
                    "rasoulzadeh-2022-strokes2surface",
                    "rasoulzadeh-2024-strokes2surface",
                    "reisinger-2023-iad",
                    "Schwengerer_Mathias-2022-BGL",
                    "steinkellner-2022-dll",
                    "ulschmid-2024-reo",
                    "ulschmid-2025-apc",
                    "unterguggenberger-2023-vaw",
                    "wimmer-2022-acd",
                    "wimmer-2023-acd",
                    "Zezulka_Matthias-2023-MatLabOptInterface",
                    "Zezulka-2025-NRL",
                    "Zezulka-2026-FGTS",
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                    "zhang-thesis"
                ],
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        "tu_id": null,
        "repositum_id": "20.500.12708/199275",
        "title": "Fast Rendering of Parametric Objects on Modern GPUs",
        "date": "2024-05",
        "abstract": "Parametric functions are an extremely efficient representation for 3D geometry, capable of compactly modelling highly complex objects. Once specified, parametric 3D objects allow for visualization at arbitrary levels of detail, at no additional memory cost, limited only by the amount of evaluated samples. However, mapping the sample evaluation to the hardware rendering pipelines of modern graphics processing units (GPUs) is not trivial. This has given rise to several specialized solutions, each targeting interactive rendering of a constrained set of parametric functions. In this paper, we propose a general method for efficient rendering of parametrically defined 3D objects. Our solution is carefully designed around modern hardware architecture. Our method adaptively analyzes, allocates and evaluates parametric function samples to produce high-quality renderings. Geometric precision can be modulated from few pixels down to sub-pixel level, enabling real-time frame rates of several 100 frames per second (FPS) for various parametric functions. We propose a dedicated level-of-detail (LOD) stage, which outputs patches of similar geometric detail to a subsequent rendering stage that uses either a hardware tessellation-based approach or performs point-based softare rasterization. Our method requires neither preprocessing nor caching, and the proposed LOD mechanism is fast enough to run each frame. Hence, our approach also lends itself to animated parametric objects. We demonstrate the benefits of our method over a state-of-the-art spherical harmonics (SH) glyph rendering method, while showing its flexibility on a range of other demanding shapes.",
        "authors_et_al": false,
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        "main_image": {
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            "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/unterguggenberger-2024-fropo/unterguggenberger-2024-fropo-image:thumb{{size}}.png"
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                "firstname": "Johannes",
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                "searchname": "Lukas Lipp",
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                "name": "Lukas Lipp",
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                "short_abstract": "<p class=\"text-align-justify\">Point clouds are a quintessential 3D geometry representation format, and often the first model obtained from reconstructive efforts, such as LIDAR scans. IVILPC aims for fast, authentic, interactive, and high-quality processing of such point-based data sets. Our project explores high-performance software rendering routines for various point-based primitives, such as point sprites, gaussian splats, surfels, and particle systems. Beyond conventional use cases, point cloud rendering also forms a key component of point-based machine learning methods and novel-view synthesis, where performance is paramount. We will exploit the flexibility and processing power of cutting-edge GPU architecture features to formulate novel, high-performance rendering approaches. The envisioned solutions will be applicable to unstructured point clouds for instant rendering of billions of points. Our research targets minimally-invasive compression, culling methods, and level-of-detail techniques for point-based rendering to deliver high performance and quality on-demand. We explore GPU-accelerated editing of point clouds, as well as common display issues on next-generation display devices. IVILPC lays the foundation for interaction with large point clouds in conventional and immersive environments. Its goal is an efficient data knowledge transfer from sensor to user, with a wide range of use cases to image-based rendering, virtual reality (VR) technology, architecture, the geospatial industry, and cultural heritage.</p>\r\n",
                "abstract": "<p class=\"text-align-justify\">Point clouds are a quintessential 3D geometry representation format, and often the first model obtained from reconstructive efforts, such as LIDAR scans. IVILPC aims for fast, authentic, interactive, and high-quality processing of such point-based data sets. Our project explores high-performance software rendering routines for various point-based primitives, such as point sprites, gaussian splats, surfels, and particle systems. Beyond conventional use cases, point cloud rendering also forms a key component of point-based machine learning methods and novel-view synthesis, where performance is paramount. We will exploit the flexibility and processing power of cutting-edge GPU architecture features to formulate novel, high-performance rendering approaches. The envisioned solutions will be applicable to unstructured point clouds for instant rendering of billions of points. Our research targets minimally-invasive compression, culling methods, and level-of-detail techniques for point-based rendering to deliver high performance and quality on-demand. We explore GPU-accelerated editing of point clouds, as well as common display issues on next-generation display devices. IVILPC lays the foundation for interaction with large point clouds in conventional and immersive environments. Its goal is an efficient data knowledge transfer from sensor to user, with a wide range of use cases to image-based rendering, virtual reality (VR) technology, architecture, the geospatial industry, and cultural heritage.</p>\r\n",
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                    "EU U-CREATE",
                    "EU Virtual Showcases",
                    "EVOCATION",
                    "GameTools",
                    "GAMEWORLD",
                    "GPV",
                    "Harvest4D",
                    "INTRA-SPACE",
                    "Landspotting",
                    "MAKE-IT-FAB",
                    "MARG",
                    "Mofa",
                    "OpenData",
                    "PAMINA",
                    "PathSpace",
                    "Photo-Guide",
                    "realreflect",
                    "REPLICATE",
                    "RESHADE",
                    "Scanopy",
                    "SCI_BIM",
                    "ShapeAcquisition",
                    "Skydome",
                    "Superhumans",
                    "TERAPOINTS",
                    "Treelumination",
                    "UrbanViz",
                    "Visual Computing",
                    "Wohnen 4.0",
                    "WorldScale"
                ],
                "research_areas": [
                    "Fabrication",
                    "Geometry",
                    "Modeling",
                    "Perception",
                    "Rendering",
                    "VR"
                ],
                "publications": [
                    "2011scherzerE",
                    "2018_Mautner_Praktikum",
                    "ABG25",
                    "ADORJAN-2013-ASE",
                    "Adorjan-2015",
                    "agyemang-2021-bsdfproxyppg",
                    "aichner-2016-sadf",
                    "albillos-2013-MA",
                    "Ams25",
                    "antes-2026-maa",
                    "arikan-2013-osn",
                    "arikan-2014-pcvis",
                    "arikan-2015-dmrt",
                    "arikan-thesis",
                    "Arleo-2019-vis",
                    "Artusi-2001-Col",
                    "Artusi-2003-CCG",
                    "Artusi-2003-Col",
                    "Artusi-2003-Del",
                    "ASCHL-2022-OTV",
                    "AUER-2010-PPT3D",
                    "ausserer-2026-baa",
                    "Auzinger_2012_AAA",
                    "Auzinger_2012_GeigerCam",
                    "Auzinger_2013_AnaVis",
                    "Auzinger_2013_NSAA",
                    "Auzinger_2013_SAR",
                    "Auzinger_2014_DCGI",
                    "Auzinger_2014_UJA",
                    "Auzinger-2014-WiGr",
                    "Auzinger-2015-IST",
                    "auzinger-2015-phd",
                    "auzinger-2015-utokyo",
                    "auzinger-dissertation",
                    "auzinger-thesis",
                    "bagar_2008",
                    "BAGAR-2010-LPB",
                    "bagar2010",
                    "balvan-rsf",
                    "BARSUKOV-2010-NEPC",
                    "bauchinger-2007-mre",
                    "Bauer_Dominik_2018_VR",
                    "BAUER-2009-AFSS",
                    "BAUER-2024-PWG",
                    "BAUER-2025-PCW",
                    "beck-2007-melk",
                    "beham_2009.2009-EVS",
                    "BEHAM-2014-RCR",
                    "beleznai_csaba-2025-pr",
                    "belic_dejan_pr",
                    "Bernhard_Matthias_2014_VAC",
                    "bernhard-2010-gph",
                    "bernhard-2011-bmtf",
                    "bernhard-2011-bmtf",
                    "bernhard-2011-maicg",
                    "bernhard-2014-EFD",
                    "bernhard-2014-GTOM",
                    "bhagvat-09-frusta",
                    "birsak-2013-sta",
                    "birsak-2014-agtb",
                    "birsak-2017-dpe",
                    "birsak-2017-dpe",
                    "Birsak-Michael-2012",
                    "birsak-thesis",
                    "birsak-thesis",
                    "Birsak2018-SA",
                    "Bittner-2001-Vis",
                    "Bittner-2003-Vis",
                    "Bittner-2004-CHC",
                    "bittner-2005-egsr",
                    "bittner-2005-egsr",
                    "BITTNER-2009-AGVS",
                    "BITTNER-2009-GEFOC",
                    "bittner-2011-scc",
                    "bodzenta-2026-baa",
                    "Bogner-2008-MTh",
                    "boindl_2013",
                    "BOKSANSKY-2019-RTS",
                    "BORZA-2012-Stellarium",
                    "brandmair-2024-bind",
                    "brandmair-2024-rust",
                    "brandmair-2025-rub",
                    "Brandorff_06_PON",
                    "Brandorff_06_SKIN",
                    "brenner_simon-2013-ba",
                    "brugger-2020-tdp",
                    "brugger-2020-tsdpbr",
                    "brugger-2020-tsdpbr",
                    "brunner_lukas_2022_ssb",
                    "Brusi-2001-Opt",
                    "CADIK-2006-IAQ",
                    "CADIK-2008-EHD",
                    "Calatrava_2013_GPGPUSOA",
                    "cardoso-2021-cost",
                    "cardoso-2022-rtpercept",
                    "cardoso-2024-r-c",
                    "cardoso-thesis",
                    "cech-2023-taa",
                    "celarek_adam-2012-rrmro",
                    "celarek_adam-2019-qelta",
                    "celarek_adam-2019-qelta",
                    "CELAREK-2017-QCL",
                    "CELAREK-2017-QCL",
                    "celarek-2022-gmcn",
                    "celarek-2022-gmcn",
                    "celarek-2022-gmcn",
                    "celarek-thesis",
                    "CHARALAMBOS-2007-HLOD",
                    "charpenay-2013-PR",
                    "charpenay-2014-sgn",
                    "Che-2025",
                    "CHIU-2008-PEN",
                    "clemenz_2019_rosy_fields",
                    "CORNEL-2010-VIS",
                    "CORNEL-2014-AFRS",
                    "CORNEL-2017-FRS",
                    "CORNEL-2019-IVF",
                    "csecsy-fcd",
                    "darovskikh_leonid-2025-prj",
                    "dataset",
                    "dataset-cat",
                    "dataset-chair",
                    "dataset-dragon",
                    "dataset-duck",
                    "dataset-ferris-wheel",
                    "dataset-penguin",
                    "dataset-strauss",
                    "dataset-tramway",
                    "dataset-viennacity",
                    "daubner-2007-HDR",
                    "deak-a3",
                    "deix-2008-rcp",
                    "dellmour-2013-grs",
                    "depner_dennis-2020-baa",
                    "depner_dennis-2022-maa",
                    "Derflinger-2024-RTTW",
                    "Devlin-2002-STA",
                    "dodik-2017-pcbpt",
                    "Doerflinger-2016-CDV",
                    "Dogangonul-2013-sparkGL",
                    "druml-2010-PFL",
                    "Druml-2013-3DrC",
                    "dusberger-2010-msm",
                    "ecormier-nocca-2025-sls",
                    "eibens-2021-tsgpmg",
                    "eibensteiner-2016-pmg",
                    "eibensteiner-2021-pmcg",
                    "eickmeyer-2027-tcd",
                    "EISEMANN-2011-RTS",
                    "EISEMANN-2012-ERT",
                    "EISEMANN-2013-ERT",
                    "elagrod_ahmed-baa",
                    "Erhardt_2025_Scutes",
                    "Erhardt_Seirlehner-2024-TamashiiIntegration",
                    "erler_2024_ppsurf",
                    "erler_2024_ppsurf",
                    "erler_2024_ppsurf",
                    "erler_2024_ppsurf",
                    "erler_philipp-2016-femfluid-prakt",
                    "erler_philipp-2017-phd",
                    "erler_philipp-2017-phd",
                    "erler_philipp-2017-phd",
                    "ERLER-2017-HVR",
                    "erler-2020-p2s",
                    "erler-2020-p2s",
                    "erler-2020-p2s",
                    "erler-2025-lidarscout",
                    "ERNST-2010-IOOC",
                    "ernst-2013-fif",
                    "ESBERGER-2016-AHO",
                    "ESCHNER-2019-GDT",
                    "fan-2014-scfl",
                    "fan-2014-scfl",
                    "Fauster-06-st",
                    "Felberbauer_2012_AG",
                    "fiedler-2009-php",
                    "fiedler-2022-vlk",
                    "fink-2012-cg1",
                    "fink-2013-cag",
                    "fink-2013-gvp",
                    "Fischer-2018-sssvr",
                    "fitzinger_andreas-daa",
                    "fizimayer-2007-art",
                    "fleiss-2015-da",
                    "forsythe-2016-ccm",
                    "FRAISS-2017-PCU",
                    "fraiss-2019-rtx",
                    "FRAISS-2022-CGMM",
                    "fraissl_niklas-2023-pra",
                    "freude_2020_rs",
                    "Freude_MSc",
                    "FREUDE-2015-phd",
                    "freude-2023-prh",
                    "freude-2023-prh",
                    "freude-2023-sem",
                    "freude-2023-sem",
                    "freude-2025-iso",
                    "freude-2025-iso",
                    "freude-2025-ras",
                    "Fritz-2012",
                    "fruehstueck-2008-gpu",
                    "fuchs_peter-2024-baa",
                    "fuericht_2025_prj",
                    "fuericht_elias-2025-prj",
                    "Fuericht-2026-FPD",
                    "Fuerst_2014_BSc",
                    "Fuerst-2009-dfr",
                    "FUERST-2013-RTWS",
                    "Fuerst-Bachelor-2013",
                    "G_P_06_RFS",
                    "GAHR-2013-AGM",
                    "gamper_2008_or",
                    "GAMPER-2018-OSG",
                    "gamsjaeger-2022-proc",
                    "gangl-2018-cgbase",
                    "GANTNER-2018-ARO",
                    "GANTNER-2019-CRS",
                    "GEBHART-2008-AGV",
                    "gersthofer_2017_COP",
                    "gersthofer-2016-sosob",
                    "geyer_lukas_2015_prj",
                    "geyer_lukas-2014-bat",
                    "geyer-2018-apbf",
                    "giefing_juergen-2013-ba",
                    "GIEGL-2006-QVS",
                    "GIEGL-2007-FVS",
                    "GIEGL-2007-QV1",
                    "GIEGL-2007-UNP",
                    "gilmutdinov-2022-aomlbwug",
                    "glanz-2017-pbrcomparison",
                    "goel-2024-rdr",
                    "goetz_johann-cagmfp",
                    "Goldmann_2011_GPU",
                    "gramegna-rfr",
                    "Grasberger_08_VV",
                    "Grasberger_2008_ISR",
                    "grasberger_2009_CSB",
                    "GRELAUD-2009-EPA",
                    "GRIESHOFER-2018-GOS",
                    "Grisenti-2026-HSHPG",
                    "grosse-2009-avp",
                    "Grossfurtner-2023-paego",
                    "grossmann-2016-baa",
                    "gruber_horst-2019-baa",
                    "Gruber2019",
                    "Guerrero_Paul_2014_EPG",
                    "guerrero-2008-dip",
                    "guerrero-2008-sli",
                    "Guerrero-2014-GRF",
                    "Guerrero-2014-TPS",
                    "guerrero-2015-lsp",
                    "gurtler-2014-ssggm",
                    "Gusenbauer_Matthias_2015_ANM",
                    "Habel_07_xms",
                    "Habel_08_SSH",
                    "Habel_09_PGT",
                    "Habel_09_PGT",
                    "Habel_2007_IAG",
                    "Habel_2007_RTT",
                    "Habel_2009_PhD",
                    "Habel_2012_PSP",
                    "Habel_LSN_2011",
                    "Habel_RAV_2010",
                    "Habel-09-RAT",
                    "Habel-09-THB",
                    "Habel-2010-EIN",
                    "hafner_christian-2013-oflow",
                    "hafner-2015-eigf",
                    "HAFNER-2015-FEM",
                    "hafner-2015-onff",
                    "halbritter-2024-m2m",
                    "hamzic_ademir-baa",
                    "hanko-2019-ani",
                    "HANN_2022_IPT",
                    "harisch-mvs",
                    "Hartl_2006_OgC",
                    "hatzinger-2026-baa",
                    "hauer_alex-2013-ba",
                    "havran-2005-egsr",
                    "havran-2005-sccg",
                    "havran-2006-tut",
                    "Hebart-2011-ColorAdjustment",
                    "HECHER-2010-3DS",
                    "hecher-2012-MH",
                    "hecher-2014-MH",
                    "HECHER-2017-HDY",
                    "heinz-2019-psi",
                    "heiss-rfr",
                    "herzberger-2021-cmake",
                    "hofer_kerstin-2023-sketch2model",
                    "hoffer-toth-com",
                    "holom_2025-prj",
                    "Holzer-06-HDR",
                    "Honic_2021",
                    "hornacek-2010-evp",
                    "horvath_imp",
                    "houska-2020-IPGM",
                    "HU-2017-ETM",
                    "huber-2025-esl",
                    "huerbe-2019-BA",
                    "HUMMEL-2001-MSE",
                    "Ilcik_2015_LAY",
                    "ilcik-2010-ps",
                    "ilcik-2011-pmous",
                    "ilcik-2013-cipmi",
                    "ilcik-2013-pmsg",
                    "ilcik-2014-cgbpmi",
                    "ilcik-2016-cmssg",
                    "ILCIK-phdthesis",
                    "ilcikova-thesis",
                    "intel2016",
                    "Ishmukhametov_Denis_2011_EIN",
                    "jafari-2023-avk",
                    "jahrmann_klemens_KFR",
                    "JAHRMANN-2013-IGR",
                    "JAHRMANN-2015-GRASS",
                    "JAHRMANN-2016-IGR",
                    "JAHRMANN-2017-RRTG",
                    "jeschke-05-AIP",
                    "jeschke-05-AIP",
                    "jeschke-05-ARI",
                    "jeschke-05-ISTAR",
                    "jeschke-09-praguetalk",
                    "jeschke-09-rendering",
                    "jeschke-09-solver",
                    "Jeschke-2002-LEM",
                    "Jeschke-2002-LEMA",
                    "Jeschke-2002-TDM",
                    "Jeschke-2002-TDMR",
                    "JESCHKE-2007-ISC",
                    "jeschke-2010-diff",
                    "jeschke-2011-est",
                    "jeschke-2011-esttalk",
                    "jeschke-2011-talkPrague",
                    "Jia-thesis",
                    "Jimenez_SSS_2015",
                    "jira_marcel-2024-baa",
                    "JORDAKIEVA-2017-VFR",
                    "KAFKA-2014-19MRD",
                    "kaipel_nikolas-2025-baa",
                    "kapferer_2021_advancing",
                    "karampekios_anastasios-prj",
                    "Kathi-2018-VRB",
                    "kazda-tmd",
                    "Keilman2022",
                    "kendlbacher-2016",
                    "kenzel_michael_2021_cuda",
                    "kerbl_2019_planet_poster",
                    "kerbl_2021_hdg",
                    "kerbl-2020-improvencoding",
                    "kerbl-2022-cuda",
                    "kerbl-2022-trienc",
                    "kimmersdorfer-2021-mzip",
                    "kimmersdorfer-2024-vcpr",
                    "klaffenboeck-2024-rva",
                    "klaffenboeck-thesis",
                    "KLAUS-2020-PCC",
                    "klein-2020-rtgi",
                    "kment_2008_cc",
                    "knecht_2013_RSM",
                    "knecht_martin_2010_DIR",
                    "knecht_martin_2012_BRDFEstimation",
                    "knecht_martin_2012_RSMR",
                    "knecht_martin_2013_ReflRefrObjsMR",
                    "knecht_martin-2011-FPSPAR",
                    "Knecht_Schwaerzler_2008_ash",
                    "knecht-2007-ao",
                    "knecht-2009-MKN",
                    "knecht-2009-TAS",
                    "knecht-2011-CBCM",
                    "knoetzl_alexander-2025-prj",
                    "koch-2019-PR",
                    "KOCH-2021-GVS",
                    "KOCH-2021-GVSDA",
                    "koenigsberger-2024-msu",
                    "koeppel-2016-baa",
                    "Koeppl-2023-DLO",
                    "koessler-2013-BA",
                    "Kogelnig_Philip-2008-OGC",
                    "kohlhas_jakob-2023-pra",
                    "Kollmann-2015-DoF",
                    "komon-2026-rso",
                    "Komon2022",
                    "konrad-2020-baa",
                    "Kovacs_2017",
                    "kovacs-2023-ttm",
                    "kozonits_marcel-2025-baa",
                    "kralicek_sebastian_baa",
                    "KREUZER-2014-DPA",
                    "KREUZER-2015-DPA",
                    "kreuzer-2016-isf",
                    "KREUZER-2017-PBF",
                    "kristmann-2022-occ",
                    "kristmann-2023-vkl",
                    "KRISTMANN-24-JPG",
                    "kristmann-thesis",
                    "Kroesl_2020_11_09",
                    "Kroesl_Katharina_2016_PPT",
                    "kroesl-2012-rtpm",
                    "kroesl-2017-LiteMaker",
                    "kroesl-2018-DC",
                    "kroesl-2018-TVS",
                    "kroesl-2019-ICthroughVR",
                    "kroesl-2019-MoD",
                    "KROESL-2020-SVI",
                    "kroesl-2020-XREye",
                    "kubicek-2024-baa",
                    "KUE11",
                    "kuehtreiber-2010-ikph",
                    "Kugler-2021",
                    "kusternig-2009-rtr",
                    "laager_florian-2013-camr",
                    "laager_florian-2014-daa",
                    "labschuetz-2010-bms",
                    "labschuetz-2011-rsr",
                    "Lan25",
                    "landauer-2022-kido",
                    "lang_jakob-2022-baa",
                    "Langer_2013_ExactGeom",
                    "lazarevic-tci",
                    "LEHNINGER-2007-SSV",
                    "leimer_2017_rbpesc",
                    "leimer_2020-cag",
                    "leimer_2020-cag",
                    "leimer_2020-cag",
                    "leimer-2013-esopc",
                    "leimer-2014-fsco",
                    "leimer-2016-coan",
                    "leimer-2016-rpe",
                    "leimer-2018-sar",
                    "leimer-2018-sar",
                    "leimer-2023-ecd",
                    "LEOPOLD-2017-ALG",
                    "leopold-2019-vox",
                    "leutschacher_2025_prj",
                    "Liang-2023-TVR",
                    "lipp_markus-2010-DAC",
                    "LIPP-2007-CGA",
                    "LIPP-2008-IEV",
                    "LIPP-2009-PGL",
                    "LIPP-2009-PGL2",
                    "LIPP-2010-PGMS",
                    "lipp-2019-rtxq3",
                    "LIPP-2020-PHD",
                    "lipp-2024-val",
                    "lipp2011a",
                    "liu-2012-tcvd",
                    "Luidolt-2020-DA",
                    "luidolt-2020-lightperceptionVR",
                    "Luksch_2007_RHR",
                    "luksch_2008_asm",
                    "luksch_2020",
                    "LUKSCH-2009-BBC",
                    "LUKSCH-2013-FLM",
                    "LUKSCH-2014-RTR",
                    "LUKSCH-2019-IGI",
                    "macho_jonas-2024-baa",
                    "MAESTRI-2012-MRW",
                    "manpreet_kainth-2012-rus",
                    "Mantler-06-landscape",
                    "MANTLER-2007-DMBBC",
                    "marek-2011-normalest",
                    "marin-2022-sig",
                    "marin-2022-sigdt",
                    "marin-2023-pic",
                    "marin-2024-dsr",
                    "marin-2024-pcp",
                    "marin-2024-pcp",
                    "marin-2024-rcf",
                    "marin-2024-sing",
                    "marin-thesis",
                    "markovich_aleksandar-2022-maa",
                    "Martinez-2015-TTB",
                    "maschek-2010-rts",
                    "matt2011",
                    "MATTAUSCH-2006-AVC",
                    "mattausch-2007-iav",
                    "MATTAUSCH-2007-OSP",
                    "mattausch-2008-CHC",
                    "mattausch-2010-tao",
                    "mattausch-2010-var",
                    "Mattausch-2010-vcr",
                    "MATTAUSCH-2012-EOV",
                    "MATTAUSCH-2012-TIS",
                    "MATTAUSCH-2013-FSBE",
                    "MATTAUSCH-2015-CHCRT",
                    "mayer_2011_cipa",
                    "MAYER-2010-MTX",
                    "Mayer-2010-VT",
                    "mayr_patrick_maa",
                    "Mayrhauser-2016-Cnc",
                    "MAYRHAUSER-2016-SCA",
                    "mazza-2012-bakk",
                    "Meier_2024_WIALT",
                    "Meier_2024_WIALT",
                    "MeierStauffer-2013-iihf",
                    "metzger-2024-smv",
                    "micu_2025-prj",
                    "miklautsch_florian-baa",
                    "MOELLINGER-2010-SD",
                    "MORARU-2014-CPD",
                    "mortezapoor-2022-photogrammabot",
                    "Mortezapoor2021ssc",
                    "Muehlbacher_2012_RRM",
                    "Muehlbacher_2012_RTR",
                    "MuehlbacherArbesser-2009-LMDLightcuts",
                    "MUEHLBERGER-2010-AFSM",
                    "mueller-2024-rbt",
                    "mujadzic_iman_baa",
                    "mujadzic-2023-hfp",
                    "murturi_PGG",
                    "musialski_2012_aachen",
                    "musialski_2012_fice",
                    "musialski_2016_sosp",
                    "musialski-2012-icb",
                    "musialski-2012-sur",
                    "Musialski-2013-ipmum",
                    "musialski-2013-prag",
                    "musialski-2013-surcgf",
                    "musialski-2015-ista",
                    "musialski-2015-pixel",
                    "musialski-2015-souos",
                    "musialski-2015-vrvis",
                    "nawrocki-2024-nsr",
                    "negi_ashish-2023-baa",
                    "Neum03CAMR",
                    "Neum04GCM",
                    "neumann-2004-aip",
                    "Neumann-2004-HOB",
                    "neumann-2004-ipm",
                    "neumann-2006-gamma",
                    "neumann-sfd",
                    "Niederreiter_2011_SM",
                    "NIEDERREITER-2010-SIM",
                    "NK_2011",
                    "novak_martin-2018-baa",
                    "Nuernberg-2011-CBO",
                    "ohrhallinger_stefan-2011-001",
                    "ohrhallinger_stefan-2012-mle",
                    "ohrhallinger_stefan-2013-c2d",
                    "ohrhallinger_stefan-2013-smi",
                    "ohrhallinger_stefan-2018-cgf",
                    "ohrhallinger_stefan-2018-pg",
                    "ohrhallinger_stefan-2022-it1",
                    "ohrhallinger_stefan-2022-tut",
                    "ohrhallinger_stefan-2023-con",
                    "ohrhallinger_stefan-2024-inv",
                    "ohrhallinger-2016-sgp",
                    "ohrhallinger-2016-sgp",
                    "ohrhallinger-2021-egs",
                    "ohrhallinger-2022-e2t",
                    "ohrhallinger-stefan-2012-the",
                    "OPPITZ-2017-3DM",
                    "oppitz-2023-ais",
                    "OPPTIZ-2018-IVRL",
                    "OTEPKA-2020-PPC",
                    "ott-rgi",
                    "Pajer-2006-tmcg",
                    "pakkanen-2026-prj",
                    "panfili-2019-VAVR",
                    "panfili-2021-myop",
                    "pangerl_2013_GPD",
                    "papantonakis-2024-rmf",
                    "parakkat-2024-ballmerge",
                    "parakkat-2024-ballmerge",
                    "PARZER-2010-STELL",
                    "PB-VRVis-2018-005",
                    "pegrisch_2018_uvs",
                    "PERNDORFER-2015-ECS",
                    "pernsteiner-2022-msh",
                    "pernsteinre_jakob_2020_eechc",
                    "Petersen_Viktoria-2024-SBO",
                    "pezenka_lukas2012-bspcga",
                    "pichler_2022_fro",
                    "pichler_2022_fro",
                    "PIRINGER-2003-HBOQ",
                    "piskernik-2010-eud",
                    "pistellato-2023-elp",
                    "PLUCH-2009-ANP",
                    "PODARAS-2017-ACRS",
                    "Podaras-praktikum-2016",
                    "pogrzebacz-2014-gggm",
                    "pogrzebacz-2023-vkl",
                    "pointner_michael-2017-baa",
                    "pointner_simon-2022-maa",
                    "POLLAK-2016-STEREO",
                    "polzer-sog",
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                "short_abstract": "<p>In recent years, virtual and augmented reality have gained widespread attention because of newly developed head-mounted displays. For the first time, mass-market penetration seems plausible. Also, range sensors are on the verge of being integrated into smartphones, evidenced by prototypes such as the Google Tango device, making ubiquitous on-line acquisition of 3D data a possibility. The combination of these two technologies – displays and sensors – promises applications where users can directly be immersed into an experience of 3D data that was just captured live. However, the captured data needs to be processed and structured before being displayed. For example, sensor noise needs to be removed, normals need to be estimated for local surface reconstruction, etc. The challenge is that these operations involve a large amount of data, and in order to ensure a lag-free user experience, they need to be performed in real time, i.e., in just a few milliseconds per frame. In this proposal, we exploit the fact that dynamic point clouds captured in real time are often only relevant for display and interaction in the current frame and inside the current view frustum. In particular, we propose a new view-dependent data structure that permits efficient connectivity creation and traversal of unstructured data, which will speed up surface recovery, e.g. for collision detection. Classifying occlusions comes at no extra cost, which will allow quick access to occluded layers in the current view. This enables new methods to explore and manipulate dynamic 3D scenes, overcoming interaction methods that rely on physics-based metaphors like walking or flying, lifting interaction with 3D environments to a “superhuman” level.</p>\r\n",
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                "short_title": "WorldScale",
                "website": null,
                "status": "active",
                "short_abstract": "Vision: reconstruct a model of the world that permits online level-of-detail extraction.",
                "abstract": "<p>Vision: reconstruct a model of the world that permits online level-of-detail extraction. The core idea in this project is to interactively integrate sensed 3D data of varying sources and scales into a topologically clean surface. Our varying-scale model then permits online extraction of seamless levels of detail for rendering with minimal aliasing and popping artifacts. For this, we develop new topological guarantees to minimize the needed geometry. By exploiting the inherent redundancy of 2D surfaces in 3D, we design a fast way to robustly detect changes that let users better control the scan acquisition process. The topologically clean output surface and the change detection permit easy processing of the geometry for common use cases such as autonomous navigation, environment learning, augmented reality displays of georeferenced semantic information. An example application is fusing and distributing scans from the built-in sensors of multiple autonomous vehicles (ground, air), for incidental map updating as well as guaranteed efficient collision detection and tracking changes for path planning.</p>\r\n",
                "start_date": "2020-05-01",
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        "tu_id": null,
        "repositum_id": "20.500.12708/195927",
        "title": "Real-Time Editing of Path-Traced Scenes with Prioritized Re-Rendering",
        "date": "2024-02",
        "abstract": "With recent developments in GPU ray tracing performance and (AI-accelerated) noise reduction techniques, Monte Carlo Path Tracing at real-time rates becomes a viable solution for interactive 3D scene editing, with growing support in popular software. However, even for minor edits (e.g., adjusting materials or moving small objects), current solutions usually discard previous samples and the image formation process is started from scratch. In this paper, we present two adaptive, priority-based re-rendering techniques with incremental updates, prioritizing the reconstruction of regions with high importance, before gradually moving to less important regions. The suggested methods automatically identify and schedule sampling and accumulation of immediately affected regions. An extensive user study analyzes whether such prioritized renderings are beneficial to interactive scene editing, comparing them with same-time conventional re-rendering. Our evaluation shows that even with simple prio rity policies, there is a significant preference for such incremental rendering techniques for interactive editing of small objects over full-screen re-rendering with denoising.",
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                "abstract": "<p class=\"text-align-justify\">Point clouds are a quintessential 3D geometry representation format, and often the first model obtained from reconstructive efforts, such as LIDAR scans. IVILPC aims for fast, authentic, interactive, and high-quality processing of such point-based data sets. Our project explores high-performance software rendering routines for various point-based primitives, such as point sprites, gaussian splats, surfels, and particle systems. Beyond conventional use cases, point cloud rendering also forms a key component of point-based machine learning methods and novel-view synthesis, where performance is paramount. We will exploit the flexibility and processing power of cutting-edge GPU architecture features to formulate novel, high-performance rendering approaches. The envisioned solutions will be applicable to unstructured point clouds for instant rendering of billions of points. Our research targets minimally-invasive compression, culling methods, and level-of-detail techniques for point-based rendering to deliver high performance and quality on-demand. We explore GPU-accelerated editing of point clouds, as well as common display issues on next-generation display devices. IVILPC lays the foundation for interaction with large point clouds in conventional and immersive environments. Its goal is an efficient data knowledge transfer from sensor to user, with a wide range of use cases to image-based rendering, virtual reality (VR) technology, architecture, the geospatial industry, and cultural heritage.</p>\r\n",
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                "abstract": "<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Research question:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> The main research question addressed by the SFB </span><strong style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">“</strong><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Advanced Computational Design</em><strong style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">”</strong><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> is how to advance design tools and processes through multi- and interdisciplinary basic research in the areas of digital architecture, integrated building design, computer graphics and virtual reality, discrete and applied geometry, and computational mechanics.&nbsp;</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Wider research context:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> Architecture, Engineering and Construction (AEC) shapes our built environment, exerting substantial environmental, cultural and economic influence on society. However, it is among the least digitized industries, still caught in silo-thinking and sequential planning processes. The Information and Communication Technology field, on the other hand, is highly innovative, creating digital design tools that are well-founded in basic research, but often lack relevant domain knowledge, thus hardly meeting designers’ needs.</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Innovation:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> We will connect architecture, computer science, mathematics and engineering in order to develop advanced computational design tools able to incorporate implicit and explicit design knowledge. This unique combination of scientific disciplines and collaborative research with strong theoretical foundations aims to bring radical innovation in computational design by allowing immediate feedback already in early design phases and by expanding solution spaces by computing design variants that cannot be found by current methods.</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Approach:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> The proposed research is structured in three areas: Design Methodology (A1), Visual and Haptic Design Interaction (A2) and Form Finding (A3). A1 uses ontologies to describe AEC design semantics, coupled to a novel digital mixed-reality sketchbook, and an innovative implicit modeling approach based on retrieving and enhancing 2D images and 3D point clouds through transfer learning. A1 also acts as a platform for integrating and evaluating the computational tools and methods developed in A2 and A3. These areas will investigate research questions in computational design involving algorithmic solutions. A2 investigates real-time global-illumination and optimization algorithms for lighting design, as well as a new method for large-scale haptic interactions in virtual reality based on a mobile robotic platform. In A3, form finding will be explored regarding geometric, mechanical and material constraints, in particular: paneling of complex shapes by patches of certain surface classes while optimizing the number of molds; algorithms for finding new transformable quad-surfaces; mechanical models for an efficient simulation of bio-composite material systems. Furthermore, new ways of form-finding will be explored experimentally, which will allow for validating the developed algorithmic approaches and reconsidering model assumptions and constraints.</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Added value:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> The fundamental computational tools and methods to be developed will be applicable in AEC and other fields of the creative industries. </span></p>",
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        "title": "PPSurf: Combining Patches and Point Convolutions for Detailed Surface Reconstruction",
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        "abstract": "Abstract 3D surface reconstruction from point clouds is a key step in areas such as content creation, archaeology, digital cultural heritage and engineering. Current approaches either try to optimize a non-data-driven surface representation to fit the points, or learn a data-driven prior over the distribution of commonly occurring surfaces and how they correlate with potentially noisy point clouds. Data-driven methods enable robust handling of noise and typically either focus on a global or a local prior, which trade-off between robustness to noise on the global end and surface detail preservation on the local end. We propose PPSurf as a method that combines a global prior based on point convolutions and a local prior based on processing local point cloud patches. We show that this approach is robust to noise while recovering surface details more accurately than the current state-of-the-art. Our source code, pre-trained model and dataset are available at https://github.com/cg-tuwien/ppsurf.",
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        "date_from": "2020",
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        "doi": "https://doi.org/10.1111/cgf.15000",
        "event": "Eurographics 2024",
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        "html_block": "<h2>Demo</h2>\n<iframe src=\"https://perler-ppsurf.hf.space\" frameborder=\"0\" style=\"width: 100%; height: 450px;\"></iframe>                                                                      \n",
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                "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Workgroup&id=rend",
                "__class": "Workgroup"
            },
            {
                "id": "Superhumans",
                "workgroup_id": "rend",
                "drupal_id": 4166,
                "drupal_path": "/research/projects/Superhumans",
                "name": "Superhumans - Walking Through Walls",
                "name_de": null,
                "short_title": "Superhumans",
                "website": null,
                "status": "active",
                "short_abstract": "<p>In recent years, virtual and augmented reality have gained widespread attention because of newly developed head-mounted displays. For the first time, mass-market penetration seems plausible. Also, range sensors are on the verge of being integrated into smartphones, evidenced by prototypes such as the Google Tango device, making ubiquitous on-line acquisition of 3D data a possibility. The combination of these two technologies – displays and sensors – promises applications where users can directly be immersed into an experience of 3D data that was just captured live. However, the captured data needs to be processed and structured before being displayed. For example, sensor noise needs to be removed, normals need to be estimated for local surface reconstruction, etc. The challenge is that these operations involve a large amount of data, and in order to ensure a lag-free user experience, they need to be performed in real time, i.e., in just a few milliseconds per frame. In this proposal, we exploit the fact that dynamic point clouds captured in real time are often only relevant for display and interaction in the current frame and inside the current view frustum. In particular, we propose a new view-dependent data structure that permits efficient connectivity creation and traversal of unstructured data, which will speed up surface recovery, e.g. for collision detection. Classifying occlusions comes at no extra cost, which will allow quick access to occluded layers in the current view. This enables new methods to explore and manipulate dynamic 3D scenes, overcoming interaction methods that rely on physics-based metaphors like walking or flying, lifting interaction with 3D environments to a “superhuman” level.</p>\r\n",
                "abstract": "<p>In recent years, virtual and augmented reality have gained widespread attention because of newly developed head-mounted displays. For the first time, mass-market penetration seems plausible. Also, range sensors are on the verge of being integrated into smartphones, evidenced by prototypes such as the Google Tango device, making ubiquitous on-line acquisition of 3D data a possibility. The combination of these two technologies – displays and sensors – promises applications where users can directly be immersed into an experience of 3D data that was just captured live. However, the captured data needs to be processed and structured before being displayed. For example, sensor noise needs to be removed, normals need to be estimated for local surface reconstruction, etc. The challenge is that these operations involve a large amount of data, and in order to ensure a lag-free user experience, they need to be performed in real time, i.e., in just a few milliseconds per frame. In this proposal, we exploit the fact that dynamic point clouds captured in real time are often only relevant for display and interaction in the current frame and inside the current view frustum. In particular, we propose a new view-dependent data structure that permits efficient connectivity creation and traversal of unstructured data, which will speed up surface recovery, e.g. for collision detection. Classifying occlusions comes at no extra cost, which will allow quick access to occluded layers in the current view. This enables new methods to explore and manipulate dynamic 3D scenes, overcoming interaction methods that rely on physics-based metaphors like walking or flying, lifting interaction with 3D environments to a “superhuman” level.</p>\r\n",
                "start_date": "2019-09-01",
                "end_date": "2023-08-31",
                "leader_id": 948,
                "logo": {
                    "name": "logo.jpg",
                    "path": "project:Superhumans",
                    "type": "image/jpeg",
                    "size": 96578,
                    "orig_name": "logo.jpg",
                    "thumb_url": "https://www.cg.tuwien.ac.at/research/projects/Superhumans/logo:thumb{{size}}.png",
                    "thumb_image_sizes": [
                        120,
                        300
                    ]
                },
                "funding_organisations": [
                    {
                        "id": "fwf",
                        "contract_number": "P32418-N31",
                        "comment": "332.780,70 €"
                    }
                ],
                "research_areas": [
                    "Geometry",
                    "VR"
                ],
                "publications": [
                    "erler_2024_ppsurf",
                    "erler_philipp-2017-phd",
                    "erler-2020-p2s",
                    "leimer_2020-cag",
                    "marin-2024-pcp",
                    "parakkat-2024-ballmerge",
                    "rait_alexius-2021-baa",
                    "SCHUETZ-2020-MPC",
                    "schuetz-2020-PPC"
                ],
                "url": "https://www.cg.tuwien.ac.at/research/projects/Superhumans",
                "__class": "Project"
            },
            {
                "id": "ShapeAcquisition",
                "workgroup_id": "rend",
                "drupal_id": 4173,
                "drupal_path": "/research/projects/ShapeAcquisition",
                "name": "Real-Time Shape Acquisition with Sensor-Specific Precision",
                "name_de": null,
                "short_title": "ShapeAcquisition",
                "website": null,
                "status": "active",
                "short_abstract": "Acquiring shapes of physical objects in real time and with guaranteed precision to the noise model of the sensor devices.",
                "abstract": "<p>The core idea in this project is to capture the shape of physical objects in real time, with guaranteed precision, and to reconstruct the shape boundaries with minimal geometry. An example application is to let untrained users acquire shapes using emerging mobile sensing devices such as Google’s Project Tango. The user moves the sensor around the object, guided by immediate visual feedback on the input sampling quality. The output is a topologically clean mesh consisting of just the vertices required to represent its features to the desired approximation. The real-time reconstruction enables numerous geometry-processing applications to be taken online, such as shape retrieval/matching, harvesting real-world geometry into a cloud, perspective photo correction, interactive modeling, augmented reality, physics simulation, or fabrication.</p>\r\n",
                "start_date": "2015-12-01",
                "end_date": "2020-11-30",
                "leader_id": 193,
                "logo": {
                    "name": "logo.jpg",
                    "path": "project:ShapeAcquisition",
                    "type": "image/jpeg",
                    "size": 20004,
                    "orig_name": "logo.jpg",
                    "thumb_url": "https://www.cg.tuwien.ac.at/research/projects/ShapeAcquisition/logo:thumb{{size}}.png",
                    "thumb_image_sizes": [
                        120,
                        300
                    ]
                },
                "funding_organisations": [
                    {
                        "id": "fwf",
                        "contract_number": "P24600-N23",
                        "comment": null
                    }
                ],
                "research_areas": [
                    "Geometry"
                ],
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                "name": "Advanced Computational Design",
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                "abstract": "<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Research question:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> The main research question addressed by the SFB </span><strong style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">“</strong><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Advanced Computational Design</em><strong style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">”</strong><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> is how to advance design tools and processes through multi- and interdisciplinary basic research in the areas of digital architecture, integrated building design, computer graphics and virtual reality, discrete and applied geometry, and computational mechanics.&nbsp;</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Wider research context:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> Architecture, Engineering and Construction (AEC) shapes our built environment, exerting substantial environmental, cultural and economic influence on society. However, it is among the least digitized industries, still caught in silo-thinking and sequential planning processes. The Information and Communication Technology field, on the other hand, is highly innovative, creating digital design tools that are well-founded in basic research, but often lack relevant domain knowledge, thus hardly meeting designers’ needs.</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Innovation:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> We will connect architecture, computer science, mathematics and engineering in order to develop advanced computational design tools able to incorporate implicit and explicit design knowledge. This unique combination of scientific disciplines and collaborative research with strong theoretical foundations aims to bring radical innovation in computational design by allowing immediate feedback already in early design phases and by expanding solution spaces by computing design variants that cannot be found by current methods.</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Approach:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> The proposed research is structured in three areas: Design Methodology (A1), Visual and Haptic Design Interaction (A2) and Form Finding (A3). A1 uses ontologies to describe AEC design semantics, coupled to a novel digital mixed-reality sketchbook, and an innovative implicit modeling approach based on retrieving and enhancing 2D images and 3D point clouds through transfer learning. A1 also acts as a platform for integrating and evaluating the computational tools and methods developed in A2 and A3. These areas will investigate research questions in computational design involving algorithmic solutions. A2 investigates real-time global-illumination and optimization algorithms for lighting design, as well as a new method for large-scale haptic interactions in virtual reality based on a mobile robotic platform. In A3, form finding will be explored regarding geometric, mechanical and material constraints, in particular: paneling of complex shapes by patches of certain surface classes while optimizing the number of molds; algorithms for finding new transformable quad-surfaces; mechanical models for an efficient simulation of bio-composite material systems. Furthermore, new ways of form-finding will be explored experimentally, which will allow for validating the developed algorithmic approaches and reconsidering model assumptions and constraints.</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Added value:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> The fundamental computational tools and methods to be developed will be applicable in AEC and other fields of the creative industries. </span></p>",
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                "name": "Toward Optimal Path Guiding for Photorealistic Rendering",
                "name_de": "Toward Optimal Path Guiding for Photorealistic Rendering",
                "short_title": "PathGuideWWTF",
                "website": "https://www.cg.tuwien.ac.at/research/projects/PathGuideWWTF",
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                "abstract": "<p>Photorealistic rendering, which is essential for many applications, involves evaluating complicated integrals. These integrals are predominantly approximated with a Monte Carlo method called path tracing. Unfortunately, this method generally requires many samples and therefore long computation times for noiseless images. Recently, so-called path-guiding techniques have been introduced, which gather knowledge about the integrands to facilitate sampling. In this project, we seek to identify and exploit untapped potential in path guiding to provide significant improvements in this active line of research. The two principal problems in path guiding are i) representing gathered knowledge and ii) sampling using that representation. We strive toward the ideal of optimal path guiding (considering practical constraints), which leads us to our main research questions: Q1. How can we represent path-guiding knowledge optimally? Q2. How can we sample optimally using that representation? We pursue two novel key ideas for possible answers: For Q1, we cast the problem of representing knowledge as a mathematical optimization problem, enabling us to exploit the knowledge and methods from the field of optimization. For Q2, we introduce “scalable product importance sampling”: adapting sampling accuracy as required to economize on resources. We see great potential for synergies between the two ideas and for them to open up new lines of investigation, thereby inspiring many follow-up works.</p>",
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        "title": "Advanced Computational Design",
        "date": "2023-10-07",
        "abstract": "Architecture, Engineering and Construction (AEC) shapes our built environment, exerting substantial environmental, cultural and economic influence on society (New European Bauhaus initiative). However, it is among the least digitized industries, caught in silo-thinking and sequential planning processes. In contrast, Information and Communication Technology creates digital design tools that are well-founded in basic research, but often lack relevant domain knowledge. Therefore, we investigate how to advance digital design tools and design processes through multi- and interdisciplinary basic research in the areas of digital architecture, integrated building design, computer graphics and virtual reality, discrete and applied geometry, and computational mechanics. Particular focus will be placed on complexity that emerges from larger-scale structures, consideration of sustainability requirements, and deeper integration of simulations in the early design phase, where the biggest impact can be achieved.",
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                "name": "Advanced Computational Design",
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                "abstract": "<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Research question:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> The main research question addressed by the SFB </span><strong style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">“</strong><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Advanced Computational Design</em><strong style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">”</strong><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> is how to advance design tools and processes through multi- and interdisciplinary basic research in the areas of digital architecture, integrated building design, computer graphics and virtual reality, discrete and applied geometry, and computational mechanics.&nbsp;</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Wider research context:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> Architecture, Engineering and Construction (AEC) shapes our built environment, exerting substantial environmental, cultural and economic influence on society. However, it is among the least digitized industries, still caught in silo-thinking and sequential planning processes. The Information and Communication Technology field, on the other hand, is highly innovative, creating digital design tools that are well-founded in basic research, but often lack relevant domain knowledge, thus hardly meeting designers’ needs.</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Innovation:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> We will connect architecture, computer science, mathematics and engineering in order to develop advanced computational design tools able to incorporate implicit and explicit design knowledge. This unique combination of scientific disciplines and collaborative research with strong theoretical foundations aims to bring radical innovation in computational design by allowing immediate feedback already in early design phases and by expanding solution spaces by computing design variants that cannot be found by current methods.</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Approach:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> The proposed research is structured in three areas: Design Methodology (A1), Visual and Haptic Design Interaction (A2) and Form Finding (A3). A1 uses ontologies to describe AEC design semantics, coupled to a novel digital mixed-reality sketchbook, and an innovative implicit modeling approach based on retrieving and enhancing 2D images and 3D point clouds through transfer learning. A1 also acts as a platform for integrating and evaluating the computational tools and methods developed in A2 and A3. These areas will investigate research questions in computational design involving algorithmic solutions. A2 investigates real-time global-illumination and optimization algorithms for lighting design, as well as a new method for large-scale haptic interactions in virtual reality based on a mobile robotic platform. In A3, form finding will be explored regarding geometric, mechanical and material constraints, in particular: paneling of complex shapes by patches of certain surface classes while optimizing the number of molds; algorithms for finding new transformable quad-surfaces; mechanical models for an efficient simulation of bio-composite material systems. Furthermore, new ways of form-finding will be explored experimentally, which will allow for validating the developed algorithmic approaches and reconsidering model assumptions and constraints.</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Added value:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> The fundamental computational tools and methods to be developed will be applicable in AEC and other fields of the creative industries. </span></p>",
                "start_date": "2020-03-01",
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        "title": "Integrating AEC Domain-Specific Multidisciplinary Knowledge for Informed and Interactive Feedback in Early Design Stages",
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        "keywords": [
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            "Early Design Stage",
            "Mixed Reality Sketching",
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                "name": "Advanced Computational Design",
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                "short_title": "ACD",
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                "abstract": "<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Research question:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> The main research question addressed by the SFB </span><strong style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">“</strong><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Advanced Computational Design</em><strong style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">”</strong><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> is how to advance design tools and processes through multi- and interdisciplinary basic research in the areas of digital architecture, integrated building design, computer graphics and virtual reality, discrete and applied geometry, and computational mechanics.&nbsp;</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Wider research context:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> Architecture, Engineering and Construction (AEC) shapes our built environment, exerting substantial environmental, cultural and economic influence on society. However, it is among the least digitized industries, still caught in silo-thinking and sequential planning processes. The Information and Communication Technology field, on the other hand, is highly innovative, creating digital design tools that are well-founded in basic research, but often lack relevant domain knowledge, thus hardly meeting designers’ needs.</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Innovation:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> We will connect architecture, computer science, mathematics and engineering in order to develop advanced computational design tools able to incorporate implicit and explicit design knowledge. This unique combination of scientific disciplines and collaborative research with strong theoretical foundations aims to bring radical innovation in computational design by allowing immediate feedback already in early design phases and by expanding solution spaces by computing design variants that cannot be found by current methods.</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Approach:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> The proposed research is structured in three areas: Design Methodology (A1), Visual and Haptic Design Interaction (A2) and Form Finding (A3). A1 uses ontologies to describe AEC design semantics, coupled to a novel digital mixed-reality sketchbook, and an innovative implicit modeling approach based on retrieving and enhancing 2D images and 3D point clouds through transfer learning. A1 also acts as a platform for integrating and evaluating the computational tools and methods developed in A2 and A3. These areas will investigate research questions in computational design involving algorithmic solutions. A2 investigates real-time global-illumination and optimization algorithms for lighting design, as well as a new method for large-scale haptic interactions in virtual reality based on a mobile robotic platform. In A3, form finding will be explored regarding geometric, mechanical and material constraints, in particular: paneling of complex shapes by patches of certain surface classes while optimizing the number of molds; algorithms for finding new transformable quad-surfaces; mechanical models for an efficient simulation of bio-composite material systems. Furthermore, new ways of form-finding will be explored experimentally, which will allow for validating the developed algorithmic approaches and reconsidering model assumptions and constraints.</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Added value:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> The fundamental computational tools and methods to be developed will be applicable in AEC and other fields of the creative industries. </span></p>",
                "start_date": "2020-03-01",
                "end_date": "2028-02-29",
                "leader_id": 193,
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        "tu_id": null,
        "repositum_id": "20.500.12708/189820",
        "title": "A Novel Integrative Design Framework Combining 4D Sketching, Geometry Reconstruction, Micromechanics Material Modelling, and Structural Analysis",
        "date": "2023-08",
        "abstract": "State-of-the-art workflows within Architecture, Engineering, and Construction (AEC) are still caught in sequential planning processes. Digital design tools in this domain often lack proper communication between different stages of design and relevant domain knowledge. Furthermore, decisions made in the early stages of design, where sketching is used to initiate, develop, and communicate ideas, heavily impact later stages, resulting in the need for rapid feedback to the architectural designer so they can proceed with adequate knowledge about design implications. Accordingly, this paper presents research on a novel integrative design framework based on a recently developed 4D sketching interface, targeted for architectural design as a form-finding tool coupled with three modules: (1) a Geometric Modelling module, which utilises Points2Surf as a machine learning model for automatic surface mesh reconstruction from the point clouds produced by sketches, (2) a Material Modelling module, which predicts the mechanical properties of biocomposites based on multiscale micromechanics homogenisation techniques, and (3) a Structural Analysis module, which assesses the mechanical performance of the meshed structure on the basis of the predicted material properties using finite element simulations. The proposed framework is a step towards using material-informed design already in the early stages of design.",
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        "articleno": "102074",
        "doi": "10.1016/j.aei.2023.102074",
        "issn": "1873-5320",
        "journal": "Advanced Engineering Informatics",
        "publisher": "ELSEVIER SCI LTD",
        "volume": "57",
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            "3D reconstruction",
            "Biocomposite",
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            "Machine learning",
            "Material-informed",
            "Micromechanics",
            "Multiscale modelling",
            "Sketch-based interface",
            "Sketch-based modelling",
            "Structural analysis"
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        "title": "The theatre metaphor for spatial computing in architectural design",
        "date": "2023-06-20",
        "abstract": "New digital technologies require new conceptual approaches to help potential users understand existing\nand envision new use cases and applications. Moving from desktop computing to spatial computing\n(virtual, augmented, mixed and extended reality environments) also requires the introduction of new\nmetaphors. New interaction and visualisation possibilities afforded by current devices are causing virtual\nand real worlds to merge into an inseparable unity of reality and imagination.\nThere are many similarities between theatre and AEC workflows. However, the theatre process is scaled\ndown in terms of space, time, and budget, and is therefore better suited to explore innovative and\nexperimental methods. In order to conceptualise the role of a novel spatial computing drawing tool\n(MR.Sketch) in existing AEC processes, we propose the theatre metaphor, which embeds the\nconceptual foundations of the tool in a collaborative design workflow based on the cooperation of\ndifferent domain experts.\nThe metaphor proposal includes the analysis of the following theatre concepts: integrative collaboration\nwith specialists, stage infrastructure, workshops for different tools and manufacturing methods, stocks\nand the immersive experience of space and time in different scales. We illustrate the capabilities of the\ntheatre metaphor to cover the entire creation and performance process of architectural design in an\nexperimental mixed reality sketching application. The implementation of an early prototype of the\nsketching application was used to evaluate the applicability of the theatre metaphor to spatial computing.",
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        "articleno": "5370",
        "doi": "10.3390/s23125370",
        "issn": "1424-8220",
        "journal": "Sensors",
        "number": "12",
        "open_access": "yes",
        "pages": "23",
        "publisher": "MDPI",
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            {
                "id": "Rendering",
                "drupal_id": 4134,
                "name": "Rendering",
                "short_abstract": "In this area, we concentrate on algorithms that synthesize images to depict 3D models or scenes, often by simulating or approximating the physics of light.",
                "abstract": "The rendering of 3D models and scenes has been a major, long-standing challenge in computer graphics that can be divided into two major subfields. <em>Real-time rendering</em> refers to the real-time synthesis of images on commodity graphics hardware to facilitate interaction for visualizations or computer games. The field of <em>physically based rendering</em> focuses on algorithms that accurately simulate the physics of light for use in the movie industry, architectural visualizations, etc. Compared to real-time rendering methods, these algorithms generally require a considerable amount of time (i.e., hours up to days) to generate acceptable results.\n\nIn the context of real-time rendering, our work revolves around the development of algorithms to achieve higher visual quality. Furthermore, we work on techniques to accelerate physically based rendering algorithms to make them viable for interactive rendering. Another aspect of our research work revolves around tools that facilitate the work of content creators.",
                "website": "https://www.cg.tuwien.ac.at/research/area/Rendering",
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        "id": "SCHUETZ-2023-LOD",
        "type_id": "journalpaper",
        "tu_id": null,
        "repositum_id": "20.500.12708/191989",
        "title": "GPU‐Accelerated LOD Generation for Point Clouds",
        "date": "2023-06",
        "abstract": "About:\nWe introduce a GPU-accelerated LOD construction process that creates a hybrid voxel-point-based variation of the widely used layered point cloud (LPC) structure for LOD rendering and streaming. The massive performance improvements provided by the GPU allow us to improve the quality of lower LODs via color filtering while still increasing construction speed compared to the non-filtered, CPU-based state of the art.\n\n\nBackground: \nLOD structures are required to render hundreds of millions to trillions of points, but constructing them takes time.\n\n\nResults: \nLOD structures suitable for rendering and streaming are constructed at rates of about 1 billion points per second (with color filtering) to 4 billion points per second (sample-picking/random sampling, state of the art) on an RTX 3090 -- an improvement of a factor of 80 to 400 times over the CPU-based state of the art (12 million points per second). Due to being in-core, model sizes are limited to about 500 million points per 24GB memory.\n\n\nDiscussion: \nOur method currently focuses on maximizing in-core construction speed on the GPU. Issues such as out-of-core construction of arbitrarily large data sets are not addressed, but we expect it to be suitable as a component of bottom-up out-of-core LOD construction schemes.",
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            "filetitle": "CudaLOD",
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        "sync_repositum_override": null,
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        "authors": [
            {
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                "orcid_id": "0000-0002-8166-3089",
                "website": "https://www.cg.tuwien.ac.at/staff/MarkusSchütz",
                "firstname": "Markus",
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                "short_abstract": "This project will research methods to test and compare global-illumination algorithms as well as filtering algorithms, and also develop test data sets for this purpose.",
                "abstract": "Rendering photorealistic images has been a long-standing problem in computer graphics. Photorealistic image synthesis is still a vibrant and active research field with many open problems. Most offline methods use path-sampling techniques to evaluate the rendering equation to account for sophisticated light-transport effects. However, this procedure takes up to hours, where the inaccuracy of the initial estimation shows up as noise in the resulting images. In order to alleviate this, sophisticated light-transport algorithms and a number of noise-filtering techniques have been developed. Despite the fact that a large body of research exists in both directions, there are no standardized datasets that enable us to adequately assess their strengths and weaknesses. It would be of utmost importance to be able to compare existing light-transport and noise-filtering algorithms in a scientifically sound way.\nIn this project, we will therefore create such a dataset, and provide the following contributions:\n(1) a set of scene descriptions that can be used to test individual features of these systems, e.g., dealing with a variety of material models, high-resolution geometry, textured inputs, and a variety of lighting effects.\n(2) a large number of rendered images of these scenes with different noisiness, auxiliary buffers to maximize compatibility with the state-of-the-art noise filtering algorithms, and fully converged reference images for easy comparisons against the denoised outputs, and\n(3) a method to ensure parameter coverage, so that the dataset does not become prohibitively large, but still covers salient rendering configurations, that reveal the most interesting cases.\nIn summary, we propose to create a fertile ground for assessing the quality of different photorealistic rendering techniques. We believe that this would lead to significantly higher quality scientific works in the field.",
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            {
                "id": "d4314",
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                "name": "Advanced Computational Design",
                "name_de": "Advanced Computational Design",
                "short_title": "ACD",
                "website": "http://acd.tuwien.ac.at",
                "status": "active",
                "short_abstract": null,
                "abstract": "<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Research question:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> The main research question addressed by the SFB </span><strong style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">“</strong><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Advanced Computational Design</em><strong style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">”</strong><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> is how to advance design tools and processes through multi- and interdisciplinary basic research in the areas of digital architecture, integrated building design, computer graphics and virtual reality, discrete and applied geometry, and computational mechanics.&nbsp;</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Wider research context:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> Architecture, Engineering and Construction (AEC) shapes our built environment, exerting substantial environmental, cultural and economic influence on society. However, it is among the least digitized industries, still caught in silo-thinking and sequential planning processes. The Information and Communication Technology field, on the other hand, is highly innovative, creating digital design tools that are well-founded in basic research, but often lack relevant domain knowledge, thus hardly meeting designers’ needs.</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Innovation:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> We will connect architecture, computer science, mathematics and engineering in order to develop advanced computational design tools able to incorporate implicit and explicit design knowledge. This unique combination of scientific disciplines and collaborative research with strong theoretical foundations aims to bring radical innovation in computational design by allowing immediate feedback already in early design phases and by expanding solution spaces by computing design variants that cannot be found by current methods.</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Approach:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> The proposed research is structured in three areas: Design Methodology (A1), Visual and Haptic Design Interaction (A2) and Form Finding (A3). A1 uses ontologies to describe AEC design semantics, coupled to a novel digital mixed-reality sketchbook, and an innovative implicit modeling approach based on retrieving and enhancing 2D images and 3D point clouds through transfer learning. A1 also acts as a platform for integrating and evaluating the computational tools and methods developed in A2 and A3. These areas will investigate research questions in computational design involving algorithmic solutions. A2 investigates real-time global-illumination and optimization algorithms for lighting design, as well as a new method for large-scale haptic interactions in virtual reality based on a mobile robotic platform. In A3, form finding will be explored regarding geometric, mechanical and material constraints, in particular: paneling of complex shapes by patches of certain surface classes while optimizing the number of molds; algorithms for finding new transformable quad-surfaces; mechanical models for an efficient simulation of bio-composite material systems. Furthermore, new ways of form-finding will be explored experimentally, which will allow for validating the developed algorithmic approaches and reconsidering model assumptions and constraints.</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Added value:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> The fundamental computational tools and methods to be developed will be applicable in AEC and other fields of the creative industries. </span></p>",
                "start_date": "2020-03-01",
                "end_date": "2028-02-29",
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        "repositum_id": "20.500.12708/153311",
        "title": "Strokes2Surface: Recovering Curve Networks from 4D Architectural Design Sketches for Shape Inference",
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                "name": "Advanced Computational Design",
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                "abstract": "<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Research question:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> The main research question addressed by the SFB </span><strong style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">“</strong><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Advanced Computational Design</em><strong style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">”</strong><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> is how to advance design tools and processes through multi- and interdisciplinary basic research in the areas of digital architecture, integrated building design, computer graphics and virtual reality, discrete and applied geometry, and computational mechanics.&nbsp;</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Wider research context:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> Architecture, Engineering and Construction (AEC) shapes our built environment, exerting substantial environmental, cultural and economic influence on society. However, it is among the least digitized industries, still caught in silo-thinking and sequential planning processes. The Information and Communication Technology field, on the other hand, is highly innovative, creating digital design tools that are well-founded in basic research, but often lack relevant domain knowledge, thus hardly meeting designers’ needs.</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Innovation:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> We will connect architecture, computer science, mathematics and engineering in order to develop advanced computational design tools able to incorporate implicit and explicit design knowledge. This unique combination of scientific disciplines and collaborative research with strong theoretical foundations aims to bring radical innovation in computational design by allowing immediate feedback already in early design phases and by expanding solution spaces by computing design variants that cannot be found by current methods.</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Approach:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> The proposed research is structured in three areas: Design Methodology (A1), Visual and Haptic Design Interaction (A2) and Form Finding (A3). A1 uses ontologies to describe AEC design semantics, coupled to a novel digital mixed-reality sketchbook, and an innovative implicit modeling approach based on retrieving and enhancing 2D images and 3D point clouds through transfer learning. A1 also acts as a platform for integrating and evaluating the computational tools and methods developed in A2 and A3. These areas will investigate research questions in computational design involving algorithmic solutions. A2 investigates real-time global-illumination and optimization algorithms for lighting design, as well as a new method for large-scale haptic interactions in virtual reality based on a mobile robotic platform. In A3, form finding will be explored regarding geometric, mechanical and material constraints, in particular: paneling of complex shapes by patches of certain surface classes while optimizing the number of molds; algorithms for finding new transformable quad-surfaces; mechanical models for an efficient simulation of bio-composite material systems. Furthermore, new ways of form-finding will be explored experimentally, which will allow for validating the developed algorithmic approaches and reconsidering model assumptions and constraints.</span></p>\n<p><em style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\">Added value:</em><span style=\"background-color:transparent;color:rgb( 0 , 0 , 0 )\"> The fundamental computational tools and methods to be developed will be applicable in AEC and other fields of the creative industries. </span></p>",
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        "title": "Assessment of Material Layers in Building Walls Using GeoRadar",
        "date": "2022-10-09",
        "abstract": "Assessing the structure of a building with non-invasive methods is an important problem. One of the possible approaches is to use GeoRadar to examine wall structures by analyzing the data obtained from the scans. However, so far, the obtained data have to be assessed manually, relying on the experience of the user in interpreting GPR radargrams. We propose a data-driven approach to evaluate the material composition of a wall from its GPR radargrams. In order to generate training data, we use gprMax to model the scanning process. Using simulation data, we use a convolutional neural network to predict the thicknesses and dielectric properties of walls per layer. We evaluate the generalization abilities of the trained model on the data collected from real buildings.",
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                "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=193",
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        ],
        "doi": "10.3390/rs14195038",
        "event": "Radar Techniques for Structures Characterization and Monitoring",
        "issn": "2072-4292",
        "journal": "Remote Sensing",
        "number": "19",
        "open_access": "yes",
        "pages": "12",
        "pages_from": "5038",
        "publisher": "MDPI",
        "volume": "14",
        "research_areas": [
            {
                "id": "Rendering",
                "drupal_id": 4134,
                "name": "Rendering",
                "short_abstract": "In this area, we concentrate on algorithms that synthesize images to depict 3D models or scenes, often by simulating or approximating the physics of light.",
                "abstract": "The rendering of 3D models and scenes has been a major, long-standing challenge in computer graphics that can be divided into two major subfields. <em>Real-time rendering</em> refers to the real-time synthesis of images on commodity graphics hardware to facilitate interaction for visualizations or computer games. The field of <em>physically based rendering</em> focuses on algorithms that accurately simulate the physics of light for use in the movie industry, architectural visualizations, etc. Compared to real-time rendering methods, these algorithms generally require a considerable amount of time (i.e., hours up to days) to generate acceptable results.\n\nIn the context of real-time rendering, our work revolves around the development of algorithms to achieve higher visual quality. Furthermore, we work on techniques to accelerate physically based rendering algorithms to make them viable for interactive rendering. Another aspect of our research work revolves around tools that facilitate the work of content creators.",
                "website": "https://www.cg.tuwien.ac.at/research/area/Rendering",
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                "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=Rendering",
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        "keywords": [
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        "doi": "10.1145/3522625",
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                "id": "Perception",
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                "name": "Perception",
                "short_abstract": "In this area, our research focuses on understanding and using the capabilities of human perception for designing high-quality graphics algorithms and effective visualizations.",
                "abstract": "<p>In computer graphics we produce images, videos, visualizations and other content that is perceived by humans. By understanding the limits of human perception, we can design and optimize rendering algorithms that perfectly fit users' needs, tailor visualizations to make data better understandable, and effectively draw the user's attention to important information.</p>\r\n",
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                    "bernhard-2014-GTOM",
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                    "birkeland_aasmund_2014_pums",
                    "cardoso-2022-rtpercept",
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