[ { "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.", "authors_et_al": false, "substitute": null, "main_image": { "description": "Teaser Figure. ", "filetitle": "CudaLOD", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 1442, "image_height": 884, "name": "SCHUETZ-2023-LOD-CudaLOD.jpg", "type": "image/jpeg", "size": 226119, "path": "Publication:SCHUETZ-2023-LOD", "url": "https://www.cg.tuwien.ac.at/research/publications/2023/SCHUETZ-2023-LOD/SCHUETZ-2023-LOD-CudaLOD.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2023/SCHUETZ-2023-LOD/SCHUETZ-2023-LOD-CudaLOD:thumb{{size}}.png" }, "sync_repositum_override": null, "authors": [ { "id": 1116, "drupal_id": 302, "orcid_id": null, "website": "https://www.cg.tuwien.ac.at/staff/MarkusSchütz", "firstname": "Markus", "lastname": "Schütz", "searchname": "Markus Schütz", "is_teacher": false, "name": "Markus Schütz", "publications": [ "kroesl_x_card_2017", "OTEPKA-2020-PPC", "schuetz_markus-2013-pra", "SCHUETZ-2014-RRLP", "SCHUETZ-2015-HQP", "SCHUETZ-2016-POT", "schuetz-2018-PPC", 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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. 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Improvements over the state of the art are achieved by batching points in a way that a number of batch-level optimizations can be computed before rasterizing the points within the same rendering pass. These optimizations include frustum culling, level-of-detail rendering, and choosing the appropriate coordinate precision for a given batch of points directly within a compute workgroup. Adaptive coordinate precision, in conjunction with visibility buffers, reduces the number of loaded bytes for the majority of points down to 4, thus making our approach several times faster than the bandwidth-limited state of the art. Furthermore, support for LOD rendering makes our software-rasterization approach suitable for rendering arbitrarily large point clouds, and to meet the increased performance demands of virtual reality rendering. 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"publication_field": [ { "publ_id": "ERLER-2017-HVR", "field_id": "supervisor", "ordernum": 1 }, { "publ_id": "FRAISS-2017-PCU", "field_id": "supervisor", "ordernum": 1 }, { "publ_id": "gersthofer_2017_COP", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "KLAUS-2020-PCC", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "KLAUS-2020-PCC", "field_id": "supervisor", "ordernum": 1 }, { "publ_id": "probst-2023-para", "field_id": "supervisor", "ordernum": 2 }, { "publ_id": "rumpler_2019_PPC", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "Rumpler-2019-PPC", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "RUMPLTER_2019-LVR", "field_id": "supervisor", "ordernum": 1 }, { "publ_id": "SCHUETZ-2015-HQP", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "schuetz-2019-CLOD", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "SCHUETZ-2020-MPC", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "schuetz-2020-PPC", "field_id": "lecturer", "ordernum": 0 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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. ", "website": "https://www.cg.tuwien.ac.at/research/area/Geometry", "teaser": { "name": "teaser.png", "path": "research_area:Geometry", "type": "image/png", "size": 168072, "orig_name": "teaser.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/Geometry/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "rend" ], "publications": [ "aichner-2016-sadf", "arikan-2013-osn", "brunner_lukas_2022_ssb", "celarek-2022-gmcn", "clemenz_2019_rosy_fields", "cornel-2022-wih", "depner_dennis-2020-baa", "depner_dennis-2022-maa", "Doerflinger-2016-CDV", "elagrod_ahmed-baa", "erler_2024_ppsurf", "erler_philipp-2017-phd", "erler-2020-p2s", "forsythe-2016-ccm", "fraissl_niklas-2023-pra", "gamsjaeger-2022-proc", "gersthofer-2016-sosob", "geyer_lukas_2015_prj", "Grossfurtner-2023-paego", "grossmann-2016-baa", "gruber_horst-2019-baa", "Guerrero-2014-TPS", "hladky-2022-QS", "hofer_kerstin-2023-sketch2model", "ILCIK-phdthesis", "kapferer_2021_advancing", "Kellner-2021-DA", "kerbl-2022-trienc", "koeppel-2016-baa", "kohlhas_jakob-2023-pra", "konrad-2020-baa", "Kugler_2019", "laager_florian-2014-daa", "lang_jakob-2022-baa", "Laschober-2018", "leimer_2017_rbpesc", "leimer_2020-cag", "leimer-2016-rpe", "leimer-2018-sar", "loeffelmann-1995-para", "marin-2022-sig", "marin-2022-sigdt", "marin-2023-pic", "markovich_aleksandar-2022-maa", "mayr_patrick_maa", "Mayrhauser-2016-Cnc", "mazza-2012-bakk", "mujadzic-2023-hfp", "murturi_PGG", "musialski_2016_sosp", "musialski-2012-sur", "musialski-2013-surcgf", "negi_ashish-2023-baa", "novak_martin-2018-baa", "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-2019-xms", "ohrhallinger_stefan-2022-tut", "ohrhallinger_stefan-2023-con", "ohrhallinger-2016-sgp", "ohrhallinger-2021-egs", "ohrhallinger-stefan-2012-the", "pegrisch_2018_uvs", "pichler_2022_fro", "pointner_michael-2017-baa", "pointner_simon-2022-maa", "preiner_2012_AS", "preiner_2017_phd", "Preiner_2019", "preiner2014clop", "prohaska_julia-2023-baa", "Radwan-2014-CDR", "Radwan-2017-Occ", "radwan-thesis", "rait_alexius-2021-baa", "REICHHOLF-2023-HRO", "reiher_joerg-2023-pra", "reinwald-2017-baa", "Reisner_2013_RSB", "riegler_2020_framework", "roth_vdi", "rubik_michael-2022-baa", "rumpelnik_martin_2020_PRM", "schaffarz_2018-pra", "schartmueller_2018_baa", "Scheiblauer-2008-DCW", "SCHEIBLAUER-2009-IDCE", "scheiblauer-2011-cag", "scheiblauer-2011-chnt", "scheiblauer-2012-chnt", "scheiblauer-2013-wscg", "Schmalstieg-1996-LOB", "Schmalstieg-1997-SMO", "scholz_2017_bac", "SCHUETZ-2022-PCC", "schuh-2018-mesh-blending", "sippl-2014-fss", "steiner_2016_isad", "steinschauer-2020-baa", "steinschorn-2023-p2m", "Tobler-1995-AAC", "Tobler-1995-BGD", "tobler02_amrmg", "Traxler-1996-CTB", "unterguggenberger-2021-msh", "Vucenovic_2022", "WEYDEMANN-2016-NUO", "wiesinger_klemens-2023-baa", "Wimmer-1998-SMO", "WIMMER-2006-DWN", "WIMMER-2006-IP", "WIMMER-2016-HARVEST4D", "withalm_brigitte-2022-baa", "zeilinger_matthias-2020-baa" ], "projects": [ "3DSpatialization", "d4388", "d9275", "EVOCATION", "GameTools", "Harvest4D", "SCI_BIM", "ShapeAcquisition", "Superhumans", "TERAPOINTS", "WorldScale" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=Geometry", "__class": "ResearchArea" }, { "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", "Adorjan-2015", "agyemang-2021-bsdfproxyppg", "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", "Bauer_Dominik_2018_VR", "bernhard-2011-bmtf", "bernhard-2014-GTOM", "bhagvat-09-frusta", "birsak-2013-sta", "Bittner-2001-Vis", "Bittner-2003-Vis", "Bittner-2004-CHC", "bittner-2005-egsr", "BITTNER-2009-AGVS", "BITTNER-2009-GEFOC", "bittner-2011-scc", "BOKSANSKY-2019-RTS", "brugger-2020-tdp", "brugger-2020-tsdpbr", "Brusi-2001-Opt", "CADIK-2006-IAQ", "CADIK-2008-EHD", "cardoso-2021-cost", "cardoso-2022-rtpercept", "cech-2023-taa", "celarek_adam-2019-qelta", "CELAREK-2017-QCL", "CHARALAMBOS-2007-HLOD", "charpenay-2014-sgn", "cline-09-poisson", "CORNEL-2017-FRS", "CORNEL-2019-IVF", "Devlin-2002-STA", "dodik-2017-pcbpt", "EISEMANN-2011-RTS", "EISEMANN-2012-ERT", "EISEMANN-2013-ERT", "ERLER-2017-HVR", "ESBERGER-2016-AHO", "ESCHNER-2019-GDT", "fink-2012-cg1", "Fischer-2018-sssvr", "FRAISS-2017-PCU", "FRAISS-2022-CGMM", "freude_2020_rs", "FREUDE-2015-phd", "freude-2023-prh", "freude-2023-sem", "GAMPER-2018-OSG", "GANTNER-2018-ARO", "GANTNER-2019-CRS", "gersthofer_2017_COP", "geyer-2018-apbf", "GIEGL-2006-QVS", "GIEGL-2007-FVS", "GIEGL-2007-QV1", "GIEGL-2007-UNP", "gilmutdinov-2022-aomlbwug", "glanz-2017-pbrcomparison", "GRELAUD-2009-EPA", "GRIESHOFER-2018-GOS", "Groeller_2016_P3", "Grossfurtner-2023-paego", "Gruber2019", "guerrero-2008-sli", "Habel_07_xms", "Habel_08_SSH", "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-2015-FEM", "HANN_2022_IPT", "havran-2005-egsr", "havran-2005-sccg", "havran-2006-tut", "hecher-2012-MH", "hecher-2014-MH", "heinz-2019-psi", "hladky-2022-QS", "hofer_kerstin-2023-sketch2model", "horvath_imp", "houska-2020-IPGM", "huerbe-2019-BA", "intel2016", "JAHRMANN-2013-IGR", "JAHRMANN-2015-GRASS", "JAHRMANN-2016-IGR", "JAHRMANN-2017-RRTG", "jeschke-05-AIP", "jeschke-05-ARI", "jeschke-05-ISTAR", "jeschke-09-praguetalk", "jeschke-09-rendering", "Jeschke-2002-LEMA", "Jeschke-2002-TDMR", "JESCHKE-2007-ISC", "jeschke-2010-diff", "jeschke-2011-est", "Jimenez_SSS_2015", "KAFKA-2014-19MRD", "Kathi-2018-VRB", "Keilman2022", "Kellner-2021-DA", "kendlbacher-2016", "kerbl_2019_planet_poster", "kerbl_2021_hdg", "kerbl-2020-improvencoding", "kerbl-2022-trienc", "kimmersdorfer-fancyMaps-2023", "KLAUS-2020-PCC", "klein_2019_PHD", "klein_2019_PMP", "klein-2020-rtgi", "knecht_2013_RSM", "knecht_martin_2010_DIR", "knecht_martin_2012_BRDFEstimation", "knecht_martin_2012_RSMR", "knecht_martin_2013_ReflRefrObjsMR", "knecht_martin-2011-FPSPAR", "knecht-2007-ao", "knecht-2011-CBCM", "koch-2019-PR", "KOCH-2021-GVS", "KOCH-2021-GVSDA", "Koeppl-2023-DLO", "Komon2022", "Konev-FCV2018", "Kovacs_2017", "KREUZER-2015-DPA", "KREUZER-2017-PBF", "kristmann-2022-occ", "kristmann-2023-vkl", "kroesl-2017-LiteMaker", "kroesl-2018-DC", "kroesl-2018-TVS", "kroesl-2019-ICthroughVR", "kroesl-2019-ThesisFF", "KROESL-2020-SVI", "kroesl-2020-XREye", "KUE11", "Kugler-2021", "leopold-2019-vox", "Liang-2023-TVR", "lipp-2019-rtxq3", "LIPP-2020-PHD", "Luidolt-2020-DA", "luidolt-2020-lightperceptionVR", "luksch_2020", "LUKSCH-2013-FLM", "LUKSCH-2014-RTR", "LUKSCH-2019-IGI", "Mantler-06-landscape", "MANTLER-2007-DMBBC", "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", "Mazza_2021_05", "MEINDL-2015-OSR", "mindek-2017-marion", "mindek-xmas-card-2016", "murturi_PGG", "neumann-2006-gamma", "OPPITZ-2017-3DM", "oppitz-2023-ais", "OPPTIZ-2018-IVRL", "OTEPKA-2020-PPC", "panfili-2019-VAVR", "panfili-2021-myop", "PB-VRVis-2018-005", "pegrisch_2018_uvs", "PERNDORFER-2015-ECS", "pernsteiner-2022-msh", "pernsteinre_jakob_2020_eechc", "PETER-2023-PSW", "pichler_2022_fro", "pistellato-2023-elp", "pogrzebacz-2023-vkl", "pointner_michael-2017-baa", "preiner_2010_GIPC", "preiner_2017_phd", "preiner11IR", "Preymann-2022-pytamashii", "probst-2023-para", "prost-2016-molecule", "PROST-2017-RTL", "PROST-2019-RTPAL", "Przemyslaw_Gora_2016_UVU", "purgathofer-2004-real", "purgathofer-2004-some", "Purgathofer-2005-Ros", "Purgathofer-2005-Tub", "Purgathofer-2006-VRVis", "radax-2008-ir", "Rainer_2017", "Rait2022", "Reisner_Irene_2013_R3D", "reznicek-2020-fpgaray", "ROEGNER-2014-TUI", "ROEGNER-2015-IBR", "roth_2021_vdst", "roth_vdi", "roth-2023-vlk", "rumpelnik_martin_2020_PRM", "Rumpelnik_Martin-2020-NPR", "rumpler_2019_PPC", "Rumpler-2019-PPC", "RUMPLER-2021-VOGL", "SAKAI-2015-EAPI", "sakai-2021-eif", "sakai-thesis", "schedl-2012-dof", "schedl-2013-gP4", "SCHEIBLAUER-2015-WFC", "scherzer_2010a", "Scherzer-2007-PCS", "SCHERZER-2008-FSR", "scherzer-2009-isvc", "SCHERZER-2012-TCM", "scherzer-eg2010", "scherzer-thesis", "scherzer2010b", "scherzer2010d", "scherzer2010e", "scherzer2011c", "scherzer2011d", "SCHOBER-2006-IVSA", "SCHUETZ-2015-HQP", "SCHUETZ-2016-POT", "schuetz-2018-PPC", "schuetz-2019-CLOD", "schuetz-2019-LCO", "SCHUETZ-2019-PCC", "SCHUETZ-2020-MPC", "schuetz-2020-PPC", "SCHUETZ-2021-DISS", 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user-assisted automatization", "website": null, "status": "active", "short_abstract": null, "abstract": null, "start_date": "2020-12-01", "end_date": "2022-11-30", "leader_id": 193, "logo": null, "funding_organisations": [], "research_areas": [], "publications": [ "SCHUETZ-2021-PCC", "SCHUETZ-2022-PCC", "SCHUETZ-2023-LOD" ], "url": "https://www.cg.tuwien.ac.at/research/projects/d4386", "__class": "Project" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2022/SCHUETZ-2022-PCC/", "__class": "Publication" }, { "id": "SCHUETZ-2021-PCC", "type_id": "journalpaper", "tu_id": 300515, "repositum_id": "20.500.12708/58641", "title": "Rendering Point Clouds with Compute Shaders and Vertex Order Optimization", "date": "2021-07-01", "abstract": "While commodity GPUs provide a continuously growing range of features and sophisticated methods for accelerating compute jobs, many state-of-the-art solutions for point cloud rendering still rely on the provided point primitives (GL_POINTS, POINTLIST, ...) of graphics APIs for image synthesis. In this paper, we present several compute-based point cloud rendering approaches that outperform the hardware pipeline by up to an order of magnitude and achieve significantly better frame times than previous compute-based methods. Beyond basic closest-point rendering, we also introduce a fast, high-quality variant to reduce aliasing. We present and evaluate several variants of our proposed methods with different flavors of optimization, in order to ensure their applicability and achieve optimal performance on a range of platforms and architectures with varying support for novel GPU hardware features. During our experiments, the observed peak performance was reached rendering 796 million points (12.7GB) at rates of 62 to 64 frames per second (50 billion points per second, 802GB/s) on an RTX 3090 without the use of level-of-detail structures.\n\nWe further introduce an optimized vertex order for point clouds to boost the efficiency of GL_POINTS by a factor of 5x in cases where hardware rendering is compulsory. We compare different orderings and show that Morton sorted buffers are faster for some viewpoints, while shuffled vertex buffers are faster in others. In contrast, combining both approaches by first sorting according to Morton-code and shuffling the resulting sequence in batches of 128 points leads to a vertex buffer layout with high rendering performance and low sensitivity to viewpoint changes. 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The Research Cluster \"Smart Communities and Technologies\" (Smart CT) at TU Wien will provide the scientific underpinnings for next-generation complex smart city and communities infrastructures. Cities are ever-evolving, complex cyber physical systems of systems covering a magnitude of different areas. The initial concept of smart cities and communities started with cities utilizing communication technologies to deliver services to their citizens and evolved to using information technology to be smarter and more efficient about the utilization of their resources. In recent years however, information technology has changed significantly, and with it the resources and areas addressable by a smart city have broadened considerably. They now cover areas like smart buildings, smart products and production, smart traffic systems and roads, autonomous driving, smart grids for managing energy hubs and electric car utilization or urban environmental systems research.
\r\n\r\n3D spatialization creates the link between the internet of cities infrastructure and the actual 3D world in which a city is embedded in order to perform advanced computation and visualization tasks. Sensors, actuators and users are embedded in a complex 3D environment that is constantly changing. Acquiring, modeling and visualizing this dynamic 3D environment are the challenges we need to face using methods from Visual Computing and Computer Graphics. 3D Spatialization aims to make a city aware of its 3D environment, allowing it to perform spatial reasoning to solve problems like visibility, accessibility, lighting, and energy efficiency.
\r\n", "abstract": "The Research Cluster \"Smart Communities and Technologies\" (Smart CT) at TU Wien will provide the scientific underpinnings for next-generation complex smart city and communities infrastructures. Cities are ever-evolving, complex cyber physical systems of systems covering a magnitude of different areas. The initial concept of smart cities and communities started with cities utilizing communication technologies to deliver services to their citizens and evolved to using information technology to be smarter and more efficient about the utilization of their resources. In recent years however, information technology has changed significantly, and with it the resources and areas addressable by a smart city have broadened considerably. They now cover areas like smart buildings, smart products and production, smart traffic systems and roads, autonomous driving, smart grids for managing energy hubs and electric car utilization or urban environmental systems research.
\r\n\r\n3D spatialization creates the link between the internet of cities infrastructure and the actual 3D world in which a city is embedded in order to perform advanced computation and visualization tasks. Sensors, actuators and users are embedded in a complex 3D environment that is constantly changing. Acquiring, modeling and visualizing this dynamic 3D environment are the challenges we need to face using methods from Visual Computing and Computer Graphics. 3D Spatialization aims to make a city aware of its 3D environment, allowing it to perform spatial reasoning to solve problems like visibility, accessibility, lighting, and energy efficiency.
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