[ { "id": "xmas-2025", "type_id": "xmascard", "tu_id": null, "repositum_id": null, "title": " X-Mas Card 2025", "date": "2025-12-24", "abstract": "The Research Unit of Computer Graphics wishes you a Merry Christmas with this brilliantly illuminated Christmas tree.\nIts temperature field is simulated using GPU-accelerated photon tracing: millions of perfectly traced rays\ncontributing to a radiative transport operator driving a non-linear Newton-Raphson solver. In this season of light, let us\ncelebrate the beauty that emerges when physics meets festive geometry, Monte Carlo convergence brings tidings of joy,\nand your RTX card finally earns its keep doing something other than training yet another diffusion model. Wishing you\nlow RMSE in your experiments, convergence in your solvers, and just enough noise to make the magic feel real.\nMerry Christmas and a radiantly rendered New Year!", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "name": "xmas-2025-image.jpg", "type": "image/jpeg", "size": 669944, "path": "Publication:xmas-2025", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/xmas-2025/xmas-2025-image.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/xmas-2025/xmas-2025-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 5496, "drupal_id": 78598, "orcid_id": "0009-0004-1431-7030", "website": "https://www.cg.tuwien.ac.at/staff/ChuyanZhang", "firstname": "Chuyan", "lastname": "Zhang", "searchname": "Chuyan Zhang", 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"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", "schneider-sturm-2026-mia", "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", 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"https://www.cg.tuwien.ac.at/research/publications/2025/xmas-2025/", "__class": "Publication" }, { "id": "sakai-2025-stater", "type_id": "inproceedings", "tu_id": null, "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.", "authors_et_al": false, "substitute": null, "main_image": { "description": "“Veach, Bidir Room” scene by Benedikt Bitterli, dedicated to the public domain under CC0 1.0 Universal (https://benedikt-bitterli.me/resources/).", "filetitle": "Representative Image", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 1500, "image_height": 1000, "name": "sakai-2025-stater-Representative Image.png", "type": "image/png", "size": 711915, "path": "Publication:sakai-2025-stater", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/sakai-2025-stater/sakai-2025-stater-Representative Image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/sakai-2025-stater/sakai-2025-stater-Representative Image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 1129, "drupal_id": 705, "orcid_id": "0000-0003-0388-8458", "website": "https://www.cg.tuwien.ac.at/staff/HiroyukiSakai", "firstname": "Hiroyuki", "lastname": "Sakai", "searchname": "Hiroyuki Sakai", "is_teacher": false, "name": "Hiroyuki Sakai", "publications": [ "CORNEL-2017-FRS", "freude_2020_rs", "freude-2023-sem", "sakai_2013", "SAKAI-2015-EAPI", "sakai-2021-eif", "sakai-2024-asa", "sakai-2025-stater", "sakai-thesis", "xmas-2023" ], "publications_first": [], "project_leader": [], "publication_field": [ { "publ_id": "agyemang-2021-bsdfproxyppg", "field_id": "supervisor", "ordernum": 1 }, { "publ_id": "dodik-2017-pcbpt", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "eickmeyer-2027-tcd", "field_id": "supervisor", "ordernum": 1 }, { "publ_id": "glanz-2017-pbrcomparison", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "hasbay-2026-tfo", "field_id": "co_supervisor", "ordernum": 0 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"SIGGRAPH Asia 2025 Conference", "isbn": "979-8-4007-2137-3", "lecturer": [ { "id": 1129, "drupal_id": 705, "orcid_id": "0000-0003-0388-8458", "website": "https://www.cg.tuwien.ac.at/staff/HiroyukiSakai", "firstname": "Hiroyuki", "lastname": "Sakai", "searchname": "Hiroyuki Sakai", "is_teacher": false, "name": "Hiroyuki Sakai", "publications": [ "CORNEL-2017-FRS", "freude_2020_rs", "freude-2023-sem", "sakai_2013", "SAKAI-2015-EAPI", "sakai-2021-eif", "sakai-2024-asa", "sakai-2025-stater", "sakai-thesis", "xmas-2023" ], "publications_first": [], "project_leader": [], "publication_field": [ { "publ_id": "agyemang-2021-bsdfproxyppg", "field_id": "supervisor", "ordernum": 1 }, { "publ_id": "dodik-2017-pcbpt", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "eickmeyer-2027-tcd", "field_id": "supervisor", "ordernum": 1 }, { "publ_id": "glanz-2017-pbrcomparison", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "hasbay-2026-tfo", "field_id": "co_supervisor", "ordernum": 0 }, { 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Real-time rendering refers to the real-time synthesis of images on commodity graphics hardware to facilitate interaction for visualizations or computer games. The field of physically based rendering 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", "teaser": { "name": "teaser.png", "path": "research_area:Rendering", "type": "image/png", "size": 5244928, "orig_name": "teaser.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/Rendering/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "rend" ], "publications": [ "2011scherzerE", "2018_Mautner_Praktikum", "ABG25", "Adorjan-2015", "agyemang-2021-bsdfproxyppg", "Ams25", "antes-g", "arikan-2014-pcvis", "arikan-2015-dmrt", "Artusi-2003-Del", "ASCHL-2022-OTV", "Auzinger_2012_AAA", "Auzinger_2012_GeigerCam", "Auzinger_2013_AnaVis", "Auzinger_2013_NSAA", "Auzinger_2013_SAR", "Auzinger_2014_DCGI", "Auzinger_2014_UJA", "Auzinger-2015-IST", "bagar2010", "balvan-g", "Bauer_Dominik_2018_VR", "BAUER-2024-PWG", "BAUER-2025-PCW", "bernhard-2011-bmtf", 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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.

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It is, therefore, important to efficiently simulate thermal effects early on and rectify possible problems. In this paper, we present an inverse simulation of radiative heat transport and a differentiable photon-tracing approach. Our method utilizes GPU-accelerated ray tracing to speed up both the forward and adjoint simulation. Moreover, we incorporate matrix compression to further increase the efficiency of our thermal solver and support larger scenes. In addition to our differentiable photon-tracing approach, we introduce a novel approximate edge sampling scheme that re-uses primary samples instead of relying on explicit edge samples or auxiliary rays to resolve visibility discontinuities. Our inverse simulation system enables designers to not only predict the temperature distribution, but also automatically optimize the design to improve thermal comfort and avoid problematic configurations. 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"ordernum": 0 } ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=1128", "__class": "Author" } ], "number": "2", "open_access": "yes", "pages": "14", "publisher": "WILEY", "volume": "44", "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. Real-time rendering refers to the real-time synthesis of images on commodity graphics hardware to facilitate interaction for visualizations or computer games. The field of physically based rendering 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", "teaser": { "name": "teaser.png", "path": "research_area:Rendering", "type": "image/png", "size": 5244928, "orig_name": "teaser.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/Rendering/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "rend" ], "publications": [ "2011scherzerE", "2018_Mautner_Praktikum", "ABG25", "Adorjan-2015", "agyemang-2021-bsdfproxyppg", "Ams25", "antes-g", "arikan-2014-pcvis", "arikan-2015-dmrt", "Artusi-2003-Del", "ASCHL-2022-OTV", "Auzinger_2012_AAA", "Auzinger_2012_GeigerCam", "Auzinger_2013_AnaVis", "Auzinger_2013_NSAA", "Auzinger_2013_SAR", "Auzinger_2014_DCGI", "Auzinger_2014_UJA", "Auzinger-2015-IST", "bagar2010", "balvan-g", "Bauer_Dominik_2018_VR", "BAUER-2024-PWG", "BAUER-2025-PCW", "bernhard-2011-bmtf", 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"zsolnai-ist-invited-2014", "zsolnaifeher-2019-pme", "zsolnaifeher-2020-pme" ], "projects": [ "3DSpatialization", "d9259", "d9275", "d9555", "Desiree", "EVOCATION", "GameTools", "GPV", "Harvest4D", "Mofa", "OpenData", "PathSpace", "REPLICATE", "RESHADE", "Scanopy", "TERAPOINTS", "Treelumination" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=Rendering", "__class": "ResearchArea" } ], "keywords": [ "Ray tracing", "Physical simulation", "Computer-aided design" ], "weblinks": [], "files": [ { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 960, "image_height": 540, "name": "freude-2025-iso-image.png", "type": "image/png", "size": 743750, "path": "Publication:freude-2025-iso", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/freude-2025-iso/freude-2025-iso-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/freude-2025-iso/freude-2025-iso-image:thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "freude-2025-iso-paper.pdf", "type": "application/pdf", "size": 13713470, "path": "Publication:freude-2025-iso", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/freude-2025-iso/freude-2025-iso-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/freude-2025-iso/freude-2025-iso-paper:thumb{{size}}.png" } ], "projects_workgroups": [ { "id": "d4314", "workgroup_id": "rend", "drupal_id": 4314, "drupal_path": "/research/projects/ACD", "name": "Advanced Computational Design", "name_de": "Advanced Computational Design", "short_title": "ACD", "website": "http://acd.tuwien.ac.at", "status": "active", "short_abstract": null, "abstract": "

Research question: The main research question addressed by the SFB “Advanced Computational Design” 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. 

\n

Wider research context: 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.

\n

Innovation: 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.

\n

Approach: 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.

\n

Added value: The fundamental computational tools and methods to be developed will be applicable in AEC and other fields of the creative industries.

", "start_date": "2020-03-01", "end_date": "2028-02-29", "leader_id": 193, "logo": null, "funding_organisations": [], "research_areas": [], "publications": [ "Derflinger-2026-RTTW2", "ecormier-nocca-2025-sls", "Erhardt_2025_Scutes", "Erhardt_Seirlehner-2024-TamashiiIntegration", "freude-2023-prh", "freude-2023-sem", "freude-2025-iso", "Fuericht-2026-FPD", "Grisenti-2026-HSHPG", "huber-2025-esl", "koenigsberger-2024-msu", "Koeppl-2023-DLO", "kovacs-2023-ttm", "landauer-2022-kido", "LIPP-2020-PHD", "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", "Zezulka-2026-GridThermalSim", "zhang-thesis" ], "url": "https://www.cg.tuwien.ac.at/research/projects/ACD", "__class": "Project" }, { "id": "d9259", "workgroup_id": "rend", "drupal_id": 9259, "drupal_path": "/research/projects/PathGuideWWTF", "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", "status": "active", "short_abstract": null, "abstract": "

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.

", "start_date": "2023-02-01", "end_date": "2027-01-31", "leader_id": 193, "logo": { "name": "logo.png", "path": "project:d9259", "type": "image/png", "size": 3185530, "orig_name": "logo.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/projects/d9259/logo:thumb{{size}}.png", "thumb_image_sizes": [ 120, 300 ] }, "funding_organisations": [], "research_areas": [ "Rendering" ], "publications": [ "eickmeyer-2027-tcd", "freude-2023-prh", "freude-2025-iso", "hasbay-2026-tfo", "sakai-2024-asa", "sakai-2025-stater" ], "url": "https://www.cg.tuwien.ac.at/research/projects/PathGuideWWTF", "__class": "Project" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2025/freude-2025-iso/", "__class": "Publication" }, { "id": "ecormier-nocca-2025-sls", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/213965", "title": "Single-Exemplar Lighting Style Transfer via Emissive Texture Synthesis and Optimization", "date": "2025", "abstract": "Lighting is a key component in how scenes are perceived. However, many interior lighting situations are currently either handcrafted by expert designers, or simply consist of basic regular arrangements of luminaires, such as to reach uniform lighting at a predefined brightness. Our method aims to bring more interesting lighting configurations to various scenes in a semi-automatic manner designed for fast prototyping by non-expert users. Starting from a single photograph of a lighting configuration, we allow users to quickly copy and adapt a lighting style to any 3D scene. Combining image analysis, texture synthesis, and light parameter optimization, we produce a lighting design for the target 3D scene matching the input image. We validate via a user study that our results successfully transfer the desired lighting style more accurately and realistically than state-of-the-art generic style transfer methods. 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graphics that can be divided into two major subfields. 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Research question: The main research question addressed by the SFB “Advanced Computational Design” 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. 

\n

Wider research context: 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.

\n

Innovation: 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.

\n

Approach: 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.

\n

Added value: The fundamental computational tools and methods to be developed will be applicable in AEC and other fields of the creative industries.

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The problem of denoising these images has been widely studied, with most recent methods relying on data-driven, pretrained neural networks. In contrast, in this paper we propose a statistical approach to the denoising problem, treating each pixel as a random variable and reasoning about its distribution. Considering a pixel of the noisy rendered image, we formulate fast pair-wise statistical tests—based on online estimators—to decide which of the nearby pixels to exclude from the denoising filter. We show that for symmetric pixel weights and normally distributed samples, the classical Welch t-test is optimal in terms of mean squared error. We then show how to extend this result to handle non-normal distributions, using more recent confidence-interval formulations in combination with the Box-Cox transformation. Our results show that our statistical denoising approach matches the performance of state-of-the-art neural image denoising without having to resort to any computation-intensive pretraining. Furthermore, our approach easily generalizes to other quantities besides pixel intensity, which we demonstrate by showing additional applications to Russian roulette path termination and multiple importance sampling.", "authors_et_al": false, "substitute": null, "main_image": { "description": "Image illustrating the proposed denoising method, created by the paper authors. 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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.

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However, lighting design has so far received little attention in this field. In this article, we introduce a method that enables continuous optimization of the arrangement of luminaires in a 3D scene via differentiable light tracing. Our experiments show two major issues when attempting to apply existing methods from differentiable path tracing to this problem: First, many rendering methods produce images, which restricts the ability of a designer to define lighting objectives to image space. Second, most previous methods are designed for scene geometry or material optimization and have not been extensively tested for the case of optimizing light sources. Currently available differentiable ray-tracing methods do not provide satisfactory performance, even on fairly basic test cases in our experience. In this article, we propose, to the best of our knowledge, a novel adjoint light tracing method that overcomes these challenges and enables gradient-based lighting design optimization in a view-independent (camera-free) way. Thus, we allow the user to paint illumination targets directly onto the 3D scene or use existing baked illumination data (e.g., light maps). Using modern ray-tracing hardware, we achieve interactive performance. We find light tracing advantageous over path tracing in this setting, as it naturally handles irregular geometry, resulting in less noise and improved optimization convergence. We compare our adjoint gradients to state-of-the-art image-based differentiable rendering methods. We also demonstrate that our gradient data works with various common optimization algorithms, providing good convergence behaviour. 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Transactions on Graphics", "number": "3", "open_access": "yes", "pages": "16", "publisher": "ASSOC COMPUTING MACHINERY", "volume": "43", "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. Real-time rendering refers to the real-time synthesis of images on commodity graphics hardware to facilitate interaction for visualizations or computer games. The field of physically based rendering 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", "teaser": { "name": "teaser.png", "path": "research_area:Rendering", "type": "image/png", "size": 5244928, "orig_name": "teaser.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/Rendering/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "rend" ], "publications": [ "2011scherzerE", "2018_Mautner_Praktikum", "ABG25", "Adorjan-2015", "agyemang-2021-bsdfproxyppg", "Ams25", "antes-g", "arikan-2014-pcvis", "arikan-2015-dmrt", "Artusi-2003-Del", "ASCHL-2022-OTV", "Auzinger_2012_AAA", "Auzinger_2012_GeigerCam", "Auzinger_2013_AnaVis", "Auzinger_2013_NSAA", "Auzinger_2013_SAR", "Auzinger_2014_DCGI", "Auzinger_2014_UJA", "Auzinger-2015-IST", "bagar2010", "balvan-g", "Bauer_Dominik_2018_VR", "BAUER-2024-PWG", "BAUER-2025-PCW", "bernhard-2011-bmtf", 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Research question: The main research question addressed by the SFB “Advanced Computational Design” 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. 

\n

Wider research context: 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.

\n

Innovation: 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.

\n

Approach: 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.

\n

Added value: The fundamental computational tools and methods to be developed will be applicable in AEC and other fields of the creative industries.

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Research question: The main research question addressed by the SFB “Advanced Computational Design” 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. 

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Wider research context: 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.

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Innovation: 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.

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Approach: 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.

\n

Added value: The fundamental computational tools and methods to be developed will be applicable in AEC and other fields of the creative industries.

", "start_date": "2020-03-01", "end_date": "2028-02-29", "leader_id": 193, "logo": null, "funding_organisations": [], "research_areas": [], "publications": [ "Derflinger-2026-RTTW2", "ecormier-nocca-2025-sls", "Erhardt_2025_Scutes", "Erhardt_Seirlehner-2024-TamashiiIntegration", "freude-2023-prh", "freude-2023-sem", "freude-2025-iso", "Fuericht-2026-FPD", "Grisenti-2026-HSHPG", "huber-2025-esl", "koenigsberger-2024-msu", "Koeppl-2023-DLO", "kovacs-2023-ttm", "landauer-2022-kido", "LIPP-2020-PHD", "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", "Zezulka-2026-GridThermalSim", "zhang-thesis" ], "url": "https://www.cg.tuwien.ac.at/research/projects/ACD", "__class": "Project" }, { "id": "d9259", "workgroup_id": "rend", "drupal_id": 9259, "drupal_path": "/research/projects/PathGuideWWTF", "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", "status": "active", "short_abstract": null, "abstract": "

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.

", "start_date": "2023-02-01", "end_date": "2027-01-31", "leader_id": 193, "logo": { "name": "logo.png", "path": "project:d9259", "type": "image/png", "size": 3185530, "orig_name": "logo.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/projects/d9259/logo:thumb{{size}}.png", "thumb_image_sizes": [ 120, 300 ] }, "funding_organisations": [], "research_areas": [ "Rendering" ], "publications": [ "eickmeyer-2027-tcd", "freude-2023-prh", "freude-2025-iso", "hasbay-2026-tfo", "sakai-2024-asa", "sakai-2025-stater" ], "url": "https://www.cg.tuwien.ac.at/research/projects/PathGuideWWTF", "__class": "Project" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2023/freude-2023-prh/", "__class": "Publication" }, { "id": "wimmer-2022-acd", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/101881", "title": "Advanced Computational Design – digitale Methoden für die frühe Entwurfsphase", "date": "2022-08-26", "abstract": "Advanced Computational Design. The SFB Advanced Computational Design addresses the research question of how to advance design tools and processes through multi- and interdisciplinary basic research. We will develop advanced computational design tools in order to improve design quality and efficiency of processes in architecture and construction. The proposed research is structured in three areas: design methodology (A1), visual and haptic design interaction (A2) and form finding (A3). A1 focuses on the conceptual basis for new digital methods of design based on machine learning. A1 also acts as a platform for integrating and evaluating the computational tools and methods developed in A2 and A3. 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. 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 through physical experiments, which will allow for reconsidering model assumptions and constraints, validating the developed algorithmic approaches, and finding new ones.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "ACD logo", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 699, "image_height": 762, "name": "wimmer-2022-acd-ACD logo.png", "type": "image/png", "size": 203297, "path": "Publication:wimmer-2022-acd", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/wimmer-2022-acd/wimmer-2022-acd-ACD logo.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/wimmer-2022-acd/wimmer-2022-acd-ACD logo:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 193, "drupal_id": 49, "orcid_id": "0000-0002-9370-2663", 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"Jeschke-2002-LEM", "Jeschke-2002-LEMA", "Jeschke-2002-TDM", "Jeschke-2002-TDMR", "JESCHKE-2007-ISC", "Jimenez_SSS_2015", "Kathi-2018-VRB", "kerbl-2020-improvencoding", "kerbl-2022-trienc", "kerbl-2024-ah3", "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", 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Research question: The main research question addressed by the SFB “Advanced Computational Design” 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. 

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Wider research context: 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.

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Innovation: 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.

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Approach: 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.

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Added value: The fundamental computational tools and methods to be developed will be applicable in AEC and other fields of the creative industries.

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