[ { "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": [ 1129, 1128, 193, 1946 ], "booktitle": "Proceedings of the SIGGRAPH Asia 2025 Conference Papers", "date_from": "2025-12-15", "date_to": "2025-12-18", "doi": "10.1145/3757377.3763995", "event": "SIGGRAPH Asia 2025 Conference", "isbn": "979-8-4007-2137-3", "lecturer": [ 1129 ], "location": "Hong Kong", "open_access": "yes", "pages": "12", "pages_from": "1", "pages_to": "12", "publisher": "ACM", "research_areas": [ "Rendering" ], "keywords": [ "Monte Carlo rendering", "path tracing", "denoising", "image filtering", "statistics" ], "weblinks": [ { "href": "https://www.cg.tuwien.ac.at/StatER", "caption": "Project Page", "description": null, "main_file": 1 } ], "files": { "0": { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": true, "access": "public", "name": "sakai-2025-stater-paper.pdf", "type": "application/pdf", "size": 10120331, "path": "Publication:sakai-2025-stater", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/sakai-2025-stater/sakai-2025-stater-paper.pdf", "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-paper:thumb{{size}}.png" }, "1": { "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" }, "3": { "description": null, "filetitle": "Supplementary Document", "main_file": true, "use_in_gallery": false, "access": "public", "name": "sakai-2025-stater-Supplementary Document.pdf", "type": "application/pdf", "size": 29341520, "path": "Publication:sakai-2025-stater", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/sakai-2025-stater/sakai-2025-stater-Supplementary Document.pdf", "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-Supplementary Document:thumb{{size}}.png" } }, "projects_workgroups": [ "d9259" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2025/sakai-2025-stater/", "__class": "Publication" }, { "id": "freude-2025-iso", "type_id": "journalpaper", "tu_id": null, "repositum_id": "20.500.12708/216172", "title": "Inverse Simulation of Radiative Thermal Transport", "date": "2025-04-17", "abstract": "The early phase of urban planning and architectural design has a great impact on the thermal loads and characteristics of constructed buildings. 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. We showcase our approach using several examples in which we optimize the placement of buildings or their facade geometry. Our approach can be used to optimize arbitrary geometric parameterizations and supports steady-state, as well as transient simulations.", "authors_et_al": false, "substitute": null, "main_image": { "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" }, "sync_repositum_override": "date_from,date_to,event,lecturer,number,open_access,volume", "repositum_presentation_id": null, "authors": [ 1128, 1525, 5257, 1063, 193, 1946 ], "articleno": "e70048", "date_from": "2025", "date_to": "2025", "doi": "10.1111/cgf.70048", "event": "Eurographics 2025", "first_published": "2025", "issn": "1467-8659", "journal": "Computer Graphics Forum", "lecturer": [ 1128 ], "number": "2", "open_access": "yes", "pages": "14", "publisher": "WILEY", "volume": "44", "research_areas": [ "Rendering" ], "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": [ "d4314", "d9259" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2025/freude-2025-iso/", "__class": "Publication" }, { "id": "sakai-2024-asa", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/209940", "title": "A Statistical Approach to Monte Carlo Denoising", "date": "2024-12", "abstract": "The stochastic nature of modern Monte Carlo (MC) rendering methods inevitably produces noise in rendered images for a practical number of samples per pixel. 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. The “Wooden Staircase” scene has been created by Wig42 (https://blendswap.com/profile/130393) under the CC BY 3.0 license.", "filetitle": "Representative Image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1980, "image_height": 1320, "name": "sakai-2024-asa-Representative Image.jpg", "type": "image/jpeg", "size": 3344845, "path": "Publication:sakai-2024-asa", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/sakai-2024-asa/sakai-2024-asa-Representative Image.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/sakai-2024-asa/sakai-2024-asa-Representative Image:thumb{{size}}.png" }, "sync_repositum_override": "date,articleno", "repositum_presentation_id": null, "authors": [ 1129, 1128, 808, 1946, 193 ], "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", "event": "SA '24: SIGGRAPH Asia 2024", "isbn": "979-8-4007-1131-2", "lecturer": [ 1129 ], "location": "Tokyo", "open_access": "yes", "pages": "11", "publisher": "Association for Computing Machinery", "research_areas": [ "Rendering" ], "keywords": [ "Monte Carlo rendering", "path tracing", "denoising", "image filtering", "statistics" ], "weblinks": [ { "href": "https://www.cg.tuwien.ac.at/StatMC", "caption": "Project Page", "description": null, "main_file": 1 } ], "files": { "0": { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "sakai-2024-asa-paper.pdf", "type": "application/pdf", "size": 11789710, "path": "Publication:sakai-2024-asa", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/sakai-2024-asa/sakai-2024-asa-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/sakai-2024-asa/sakai-2024-asa-paper:thumb{{size}}.png" }, "1": { "description": "Image illustrating the proposed denoising method, created by the paper authors. The “Wooden Staircase” scene has been created by Wig42 (https://blendswap.com/profile/130393) under the CC BY 3.0 license.", "filetitle": "Representative Image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1980, "image_height": 1320, "name": "sakai-2024-asa-Representative Image.jpg", "type": "image/jpeg", "size": 3344845, "path": "Publication:sakai-2024-asa", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/sakai-2024-asa/sakai-2024-asa-Representative Image.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/sakai-2024-asa/sakai-2024-asa-Representative Image:thumb{{size}}.png" }, "3": { "description": null, "filetitle": "Supplementary Document", "main_file": true, "use_in_gallery": false, "access": "public", "name": "sakai-2024-asa-Supplementary Document.pdf", "type": "application/pdf", "size": 10739991, "path": "Publication:sakai-2024-asa", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/sakai-2024-asa/sakai-2024-asa-Supplementary Document.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/sakai-2024-asa/sakai-2024-asa-Supplementary Document:thumb{{size}}.png" } }, "projects_workgroups": [ "d9259" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/sakai-2024-asa/", "__class": "Publication" }, { "id": "freude-2023-prh", "type_id": "journalpaper", "tu_id": null, "repositum_id": "20.500.12708/190854", "title": "Precomputed radiative heat transport for efficient thermal simulation", "date": "2023-11", "abstract": "Architectural design and urban planning are complex design tasks. Predicting the thermal impact of design choices at interactive rates enhances the ability of designers to improve energy efficiency and avoid problematic heat islands while maintaining design quality. We show how to use and adapt methods from computer graphics to efficiently simulate heat transfer via thermal radiation, thereby improving user guidance in the early design phase of large-scale construction projects and helping to increase energy efficiency and outdoor comfort. Our method combines a hardware-accelerated photon tracing approach with a carefully selected finite element discretization, inspired by precomputed radiance transfer. This combination allows us to precompute a radiative transport operator, which we then use to rapidly solve either steady-state or transient heat transport throughout the entire scene. Our formulation integrates time-dependent solar irradiation data without requiring changes in the transport operator, allowing us to quickly analyze many different scenarios such as common weather patterns, monthly or yearly averages, or transient simulations spanning multiple days or weeks. We show how our approach can be used for interactive design workflows such as city planning via fast feedback in the early design phase.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 895, "image_height": 395, "name": "freude-2023-prh-image.png", "type": "image/png", "size": 368081, "path": "Publication:freude-2023-prh", "url": "https://www.cg.tuwien.ac.at/research/publications/2023/freude-2023-prh/freude-2023-prh-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2023/freude-2023-prh/freude-2023-prh-image:thumb{{size}}.png" }, "sync_repositum_override": "date,date_from,date_to,event,lecturer,pages_from,pages_to", "repositum_presentation_id": null, "authors": [ 1128, 1946, 1063, 1525, 193 ], "articleno": "e14957", "date_from": "2023-11", "date_to": "2023-11", "doi": "10.1111/cgf.14957", "event": "Pacific Graphics 2023", "first_published": "2023-11", "issn": "1467-8659", "journal": "Computer Graphics Forum", "lecturer": [ 1128 ], "number": "7", "open_access": "yes", "pages": "14", "publisher": "WILEY", "volume": "42", "research_areas": [ "Rendering" ], "keywords": [ "thermal radiation", "rendering", "computer graphics" ], "weblinks": [], "files": [ { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 895, "image_height": 395, "name": "freude-2023-prh-image.png", "type": "image/png", "size": 368081, "path": "Publication:freude-2023-prh", "url": "https://www.cg.tuwien.ac.at/research/publications/2023/freude-2023-prh/freude-2023-prh-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2023/freude-2023-prh/freude-2023-prh-image:thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "freude-2023-prh-paper.pdf", "type": "application/pdf", "size": 10540215, "path": "Publication:freude-2023-prh", "url": "https://www.cg.tuwien.ac.at/research/publications/2023/freude-2023-prh/freude-2023-prh-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2023/freude-2023-prh/freude-2023-prh-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "d4314", "d9259" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2023/freude-2023-prh/", "__class": "Publication" } ]