[ { "id": "freude-2023-sem", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/175763", "title": "Sampling-Distribution-Based Evaluation for Monte Carlo Rendering", "date": "2023", "abstract": "In this paper, we investigate the application of per-pixel difference metrics for evaluating Monte Carlo (MC) rendering techniques. In particular, we propose to take the sampling distribution of the mean (SDM) into account for this purpose. We establish the theoretical background and analyze other per-pixel difference metrics, such as the absolute deviation (AD) and the mean squared error (MSE) in relation to the SDM. Based on insights from this analysis, we propose a new, alternative, and particularly easy-to-use approach, which builds on the SDM and facilitates meaningful comparisons of MC rendering techniques on a per-pixel basis. In order to demonstrate the usefulness of our approach, we compare it to commonly used metrics based on a variety of images computed with different rendering techniques. Our evaluation reveals limitations of commonly used metrics, in particular regarding the detection of differences between renderings that might be difficult to detect otherwise—this circ umstance is particularly apparent in comparison to the MSE calculated for each pixel. Our results indicate the potential of SDM-based approaches to reveal differences between MC renderers that might be caused by conceptual or implementation-related issues. Thus, we understand our approach as a way to facilitate the development and evaluation of rendering techniques.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": true, "use_in_gallery": false, "access": "public", "image_width": 1028, "image_height": 1028, "name": "freude-2023-sem-image.png", "type": "image/png", "size": 731748, "path": "Publication:freude-2023-sem", "url": "https://www.cg.tuwien.ac.at/research/publications/2023/freude-2023-sem/freude-2023-sem-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2023/freude-2023-sem/freude-2023-sem-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1128, 1129, 1073, 193 ], "booktitle": "Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications", "date_from": "2023-02-19", "date_to": "2023-02-21", "doi": "10.5220/0011886300003417", "event": "18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (GRAPP)", "isbn": "978-989-758-634-7", "lecturer": [ 1128 ], "location": "Lisbon, Portugal", "open_access": "yes", "pages": "12", "pages_from": "119", "pages_to": "130", "publisher": "Scitepress", "volume": "1", "research_areas": [ "Rendering" ], "keywords": [ "computer graphics", "Rendering", "Ray Tracing", "Evaluation", "Validation" ], "weblinks": [], "files": [ { "description": null, "filetitle": "image", "main_file": true, "use_in_gallery": false, "access": "public", "image_width": 1028, "image_height": 1028, "name": "freude-2023-sem-image.png", "type": "image/png", "size": 731748, "path": "Publication:freude-2023-sem", "url": "https://www.cg.tuwien.ac.at/research/publications/2023/freude-2023-sem/freude-2023-sem-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2023/freude-2023-sem/freude-2023-sem-image:thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "freude-2023-sem-paper.pdf", "type": "application/pdf", "size": 8656300, "path": "Publication:freude-2023-sem", "url": "https://www.cg.tuwien.ac.at/research/publications/2023/freude-2023-sem/freude-2023-sem-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2023/freude-2023-sem/freude-2023-sem-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "OpenData", "d4314" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2023/freude-2023-sem/", "__class": "Publication" }, { "id": "freude_2020_rs", "type_id": "techreport", "tu_id": null, "repositum_id": null, "title": "R-Score: A Novel Approach to Compare Monte Carlo Renderings", "date": "2020-08", "abstract": "In this paper, we propose a new approach for the comparison and analysis of Monte Carlo (MC) rendering algorithms. It is based on a novel similarity measure called render score (RS) that is specically designed for MC rendering, statistically motivated, and incorporates bias and variance. Additionally, we propose a comparison scheme that alleviates the need for practically converged reference images (RIs). Our approach can be used to compare and analyze dierent rendering methods by revealing detailed (per-pixel) dierences and subsequently potential conceptual or implementation-related issues, thereby offering a more informative and meaningful alternative to commonly used metrics.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "teaser-image", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 630, "image_height": 533, "name": "freude_2020_rs-teaser-image.png", "type": "image/png", "size": 89069, "path": "Publication:freude_2020_rs", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/freude_2020_rs/freude_2020_rs-teaser-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/freude_2020_rs/freude_2020_rs-teaser-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1128, 1129, 1073, 193 ], "number": "TR-193-02-2020-4", "open_access": "no", "research_areas": [ "Rendering" ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "teaser-image", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 630, "image_height": 533, "name": "freude_2020_rs-teaser-image.png", "type": "image/png", "size": 89069, "path": "Publication:freude_2020_rs", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/freude_2020_rs/freude_2020_rs-teaser-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/freude_2020_rs/freude_2020_rs-teaser-image:thumb{{size}}.png" }, { "description": null, "filetitle": "technical-report", "main_file": true, "use_in_gallery": false, "access": "public", "name": "freude_2020_rs-technical-report.pdf", "type": "application/pdf", "size": 1215341, "path": "Publication:freude_2020_rs", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/freude_2020_rs/freude_2020_rs-technical-report.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/freude_2020_rs/freude_2020_rs-technical-report:thumb{{size}}.png" } ], "projects_workgroups": [ "OpenData" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2020/freude_2020_rs/", "__class": "Publication" }, { "id": "zsolnaifeher-2020-pme", "type_id": "journalpaper", "tu_id": null, "repositum_id": "20.500.12708/141026", "title": "Photorealistic Material Editing Through Direct Image Manipulation", "date": "2020-06-29", "abstract": "Creating photorealistic materials for light transport algorithms requires carefully fine-tuning a set of material properties to achieve a desired artistic effect. This is typically a lengthy process that involves a trained artist with specialized knowledge. In this work, we present a technique that aims to empower novice and intermediate-level users to synthesize high-quality photorealistic materials by only requiring basic image processing knowledge. In the proposed workflow, the user starts with an input image and applies a few intuitive transforms (e.g., colorization, image inpainting) within a 2D image editor of their choice, and in the next step, our technique produces a photorealistic result that approximates this target image. Our method combines the advantages of a neural network-augmented optimizer and an encoder neural network to produce high-quality output results within 30 seconds. We also demonstrate that it is resilient against poorly-edited target images and propose a simple extension to predict image sequences with a strict time budget of 1-2 seconds per image.\n\nVideo: https://www.youtube.com/watch?v=8eNHEaxsj18", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 883, "image_height": 934, "name": "zsolnaifeher-2020-pme-image.jpg", "type": "image/jpeg", "size": 239774, "path": "Publication:zsolnaifeher-2020-pme", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/zsolnaifeher-2020-pme/zsolnaifeher-2020-pme-image.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/zsolnaifeher-2020-pme/zsolnaifeher-2020-pme-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1073, 194, 193 ], "cfp": { "name": "[Members] Updated EGSR CFP.eml", "type": "message/rfc822", "error": "0", "size": "23492", "orig_name": "[Members] Updated EGSR CFP.eml", "ext": "eml" }, "date_from": "2020-06-29", "date_to": "2020-07-03", "doi": "10.1111/cgf.14057", "event": "Eurographics Symposium on Rendering 2020", "first_published": "2020-07-20", "issn": "1467-8659", "journal": "Computer Graphics Forum", "lecturer": [ 1073 ], "location": "London, UK", "number": "4", "open_access": "yes", "pages": "14", "pages_from": "107", "pages_to": "120", "volume": "39", "research_areas": [ "Rendering" ], "keywords": [ "neural rendering", "neural networks", "photorealistic rendering", "photorealistic material editing" ], "weblinks": [ { "href": "https://users.cg.tuwien.ac.at/zsolnai/gfx/photorealistic-material-editing/", "caption": "Source code", "description": null, "main_file": 0 }, { "href": "https://www.youtube.com/watch?v=8eNHEaxsj18", "caption": "Supplementary video", "description": null, "main_file": 1 } ], "files": [ { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 883, "image_height": 934, "name": "zsolnaifeher-2020-pme-image.jpg", "type": "image/jpeg", "size": 239774, "path": "Publication:zsolnaifeher-2020-pme", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/zsolnaifeher-2020-pme/zsolnaifeher-2020-pme-image.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/zsolnaifeher-2020-pme/zsolnaifeher-2020-pme-image:thumb{{size}}.png" }, { "description": null, "filetitle": "image2", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2768, "image_height": 2959, "name": "zsolnaifeher-2020-pme-image2.jpg", "type": "image/jpeg", "size": 976205, "path": "Publication:zsolnaifeher-2020-pme", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/zsolnaifeher-2020-pme/zsolnaifeher-2020-pme-image2.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/zsolnaifeher-2020-pme/zsolnaifeher-2020-pme-image2:thumb{{size}}.png" }, { "description": null, "filetitle": "image3", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2560, "image_height": 1440, "name": "zsolnaifeher-2020-pme-image3.jpg", "type": "image/jpeg", "size": 631004, "path": "Publication:zsolnaifeher-2020-pme", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/zsolnaifeher-2020-pme/zsolnaifeher-2020-pme-image3.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/zsolnaifeher-2020-pme/zsolnaifeher-2020-pme-image3:thumb{{size}}.png" }, { "description": null, "filetitle": "image4", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 2560, "image_height": 1440, "name": "zsolnaifeher-2020-pme-image4.jpg", "type": "image/jpeg", "size": 666732, "path": "Publication:zsolnaifeher-2020-pme", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/zsolnaifeher-2020-pme/zsolnaifeher-2020-pme-image4.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/zsolnaifeher-2020-pme/zsolnaifeher-2020-pme-image4:thumb{{size}}.png" }, { "description": null, "filetitle": "image5", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 3840, "image_height": 2160, "name": "zsolnaifeher-2020-pme-image5.jpg", "type": "image/jpeg", "size": 616892, "path": "Publication:zsolnaifeher-2020-pme", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/zsolnaifeher-2020-pme/zsolnaifeher-2020-pme-image5.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/zsolnaifeher-2020-pme/zsolnaifeher-2020-pme-image5:thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "zsolnaifeher-2020-pme-paper.pdf", "type": "application/pdf", "size": 11320226, "path": "Publication:zsolnaifeher-2020-pme", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/zsolnaifeher-2020-pme/zsolnaifeher-2020-pme-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/zsolnaifeher-2020-pme/zsolnaifeher-2020-pme-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "PathSpace" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2020/zsolnaifeher-2020-pme/", "__class": "Publication" }, { "id": "zsolnai-feher-thesis-2019", "type_id": "phdthesis", "tu_id": null, "repositum_id": null, "title": "Photorealistic Material Learning and Synthesis", "date": "2019-12", "abstract": "Light transport simulations are the industry-standard way of creating convincing photorealistic imagery and are widely used in creating animation movies, computer animations, medical and architectural visualizations among many other notable applications. These techniques simulate how millions of rays of light interact with a virtual scene, where the realism of the final output depends greatly on the quality of the used materials and the geometry of the objects within this scene. In this thesis, we endeavor to address two key issues pertaining to photorealistic material synthesis: first, creating convincing photorealistic materials requires years of expertise in this field and requires a non-trivial amount of trial and error from the side of the artist. We propose two learning-based methods that enables novice users to easily and quickly synthesize photorealistic materials by learning their preferences and recommending arbitrarily many new material models that are in line with their artistic vision. We also augmented these systems with a neural renderer that performs accurate light-transport simulation for these materials orders of magnitude quicker than the photorealistic rendering engines commonly used for these tasks. As a result, novice users are now able to perform mass-scale material synthesis, and even expert users experience a significant improvement in modeling times when many material models are sought.\n\nSecond, simulating subsurface light transport leads to convincing translucent material visualizations, however, most published techniques either take several hours to compute an image, or make simplifying assumptions regarding the underlying physical laws of volumetric scattering. We propose a set of real-time methods to remedy this issue by decomposing well-known 2D convolution filters into a set of separable 1D convolutions while retaining a high degree of visual accuracy. These methods execute within a few milliseconds and can be inserted into state-of-the-art rendering systems as a simple post-processing step without introducing intrusive changes into the rendering pipeline.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2688, "image_height": 1512, "name": "zsolnai-feher-thesis-2019-image.jpg", "type": "image/jpeg", "size": 1467354, "path": "Publication:zsolnai-feher-thesis-2019", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/zsolnai-feher-thesis-2019/zsolnai-feher-thesis-2019-image.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/zsolnai-feher-thesis-2019/zsolnai-feher-thesis-2019-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1073 ], "date_end": "2019", "date_start": "2013", "open_access": "yes", "reviewer_1": [ 196 ], "reviewer_2": [ 1736 ], "rigorosum": "2020-01", "supervisor": [ 193 ], "research_areas": [ "Rendering" ], "keywords": [ "neural rendering", "machine learning", "photorealistic rendering", "ray tracing", "global illumination", "material synthesis" ], "weblinks": [ { "href": "https://users.cg.tuwien.ac.at/zsolnai/", "caption": "More information, supplementary videos and materials", "description": null, "main_file": 0 } ], "files": [ { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2688, "image_height": 1512, "name": "zsolnai-feher-thesis-2019-image.jpg", "type": "image/jpeg", "size": 1467354, "path": "Publication:zsolnai-feher-thesis-2019", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/zsolnai-feher-thesis-2019/zsolnai-feher-thesis-2019-image.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/zsolnai-feher-thesis-2019/zsolnai-feher-thesis-2019-image:thumb{{size}}.png" }, { "description": null, "filetitle": "thesis", "main_file": true, "use_in_gallery": false, "access": "public", "name": "zsolnai-feher-thesis-2019-thesis.pdf", "type": "application/pdf", "size": 149439876, "path": "Publication:zsolnai-feher-thesis-2019", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/zsolnai-feher-thesis-2019/zsolnai-feher-thesis-2019-thesis.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/zsolnai-feher-thesis-2019/zsolnai-feher-thesis-2019-thesis:thumb{{size}}.png" } ], "projects_workgroups": [ "PathSpace" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2019/zsolnai-feher-thesis-2019/", "__class": "Publication" }, { "id": "zsolnai-2018-gms", "type_id": "journalpaper", "tu_id": null, "repositum_id": null, "title": "Gaussian Material Synthesis", "date": "2018-08", "abstract": "We present a learning-based system for rapid mass-scale material synthesis that is useful for novice and expert users alike. The user preferences are learned via Gaussian Process Regression and can be easily sampled for new recommendations. Typically, each recommendation takes 40-60 seconds to render with global illumination, which makes this process impracticable for real-world workflows. Our neural network eliminates this bottleneck by providing high-quality image predictions in real time, after which it is possible to pick the desired materials from a gallery and assign them to a scene in an intuitive manner. Workflow timings against Disney’s “principled” shader reveal that our system scales well with the number of sought materials, thus empowering even novice users to generate hundreds of high-quality material models without any expertise in material modeling. Similarly, expert users experience a significant decrease in the total modeling time when populating a scene with materials. Furthermore, our proposed solution also offers controllable recommendations and a novel latent space variant generation step to enable the real-time fine-tuning of materials without requiring any domain expertise.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "teaser2", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2043, "image_height": 2229, "name": "zsolnai-2018-gms-teaser2.jpg", "type": "image/jpeg", "size": 1534101, "path": "Publication:zsolnai-2018-gms", "url": "https://www.cg.tuwien.ac.at/research/publications/2018/zsolnai-2018-gms/zsolnai-2018-gms-teaser2.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2018/zsolnai-2018-gms/zsolnai-2018-gms-teaser2:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1073, 194, 193 ], "date_from": "2018-08-06", "date_to": "2018-08-12", "doi": "10.1145/3197517.3201307", "event": "SIGGRAPH 2018", "issn": "0730-0301", "journal": "ACM Transactions on Graphics (SIGGRAPH 2018)", "lecturer": [ 1073 ], "location": "Vancouver, Canada", "number": "4", "open_access": "yes", "pages_from": "76:1", "pages_to": "76:14", "volume": "37", "research_areas": [ "Rendering" ], "keywords": [ "gaussian material synthesis", "neural rendering", "neural rendering" ], "weblinks": [ { "href": "https://users.cg.tuwien.ac.at/zsolnai/gfx/gaussian-material-synthesis/", "caption": "Project webpage with source code", "description": null, "main_file": 0 } ], "files": [ { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "zsolnai-2018-gms-paper.pdf", "type": "application/pdf", "size": 53921452, "path": "Publication:zsolnai-2018-gms", "url": "https://www.cg.tuwien.ac.at/research/publications/2018/zsolnai-2018-gms/zsolnai-2018-gms-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 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