[ { "id": "celarek-2025-d3g", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/219918", "title": "Does 3D Gaussian Splatting Need Accurate Volumetric Rendering?", "date": "2025-05", "abstract": "Since its introduction, 3D Gaussian Splatting (3DGS) has become an important reference method for learning 3D representations of a captured scene, allowing real-time novel-view synthesis with high visual quality and fast training times. Neural Radiance Fields (NeRFs), which preceded 3DGS, are based on a principled ray-marching approach for volumetric rendering. In contrast, while sharing a similar image formation model with NeRF, 3DGS uses a hybrid rendering solution that builds on the strengths of volume rendering and primitive rasterization. A crucial benefit of 3DGS is its performance, achieved through a set of approximations, in many cases with respect to volumetric rendering theory. A naturally arising question is whether replacing these approximations with more principled volumetric rendering solutions can improve the quality of 3DGS. In this paper, we present an in-depth analysis of the various approximations and assumptions used by the original 3DGS solution. We demonstrate that, while more accurate volumetric rendering can help for low numbers of primitives, the power of efficient optimization and the large number of Gaussians allows 3DGS to outperform volumetric rendering despite its approximations.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1013, 5372, 5503, 193, 1650 ], "articleno": "e70032", "doi": "10.1111/cgf.70032", "issn": "1467-8659", "journal": "Computer Graphics Forum", "number": "2", "pages": "12", "publisher": "WILEY", "volume": "44", "research_areas": [], "keywords": [ "CCS Concepts", "Rasterization", "Ray tracing", "Volumetric models", "• Computing methodologies → Image-based rendering" ], "weblinks": [], "files": [], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2025/celarek-2025-d3g/", "__class": "Publication" }, { "id": "ulschmid-2025-apc", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/215609", "title": "Automated Prioritization for Context-Aware Re-rendering in Editing", "date": "2025", "abstract": "Real-time Monte Carlo path tracing has become a feasible option for interactive 3D scene editing due to recent advancements in GPU ray tracing performance, as well as (AI-accelerated) denoising techniques. While it is thus gaining increasing support in popular modeling software, even minor edits such as adjusting materials or moving small objects typically require current solutions to discard previous samples and restart the image formation process from scratch. A recent solution introduced two adaptive, priority-based re-rendering techniques implementing incremental updates while focusing first on reconstructing regions of high importance and gradually addressing less critical areas. An extensive user study compared these prioritized renderings with conventional same-time re-rendering to evaluate their effectiveness for interactive scene editing. Results indicate a significant preference for incremental rendering techniques for editing small objects over traditional full-screen re-rendering with denoising, even with basic priority policies. Building upon these results, we revisit the underlying design choices and derive more sophisticated priority policies that respect global illumination effects (shadows and reflections) as well as employing attention-based techniques (based either on eye tracking to prioritize areas in the user’s gaze or, alternatively, using the cursor position).", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1501, "image_height": 930, "name": "ulschmid-2025-apc-image.webp", "type": "image/webp", "size": 91268, "path": "Publication:ulschmid-2025-apc", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/ulschmid-2025-apc/ulschmid-2025-apc-image.webp", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/ulschmid-2025-apc/ulschmid-2025-apc-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1954, 1030, 193, 1650 ], "ac_number": "AC17528723", "articleno": "353", "doi": "10.1007/s42979-025-03863-z", "issn": "2661-8907", "journal": "SN Computer Science", "number": "2025", "open_access": "yes", "pages": "14", "publisher": "Springer Nature", "volume": "6", "research_areas": [ "Rendering" ], "keywords": [ "Path Tracing", "Scene editing", "Adaptive rendering", "empirical studies" ], "weblinks": [ { "href": "https://github.com/cg-tuwien/Prioritized-ReRendering", "caption": "GitHub", "description": "GitHub", "main_file": 1 } ], "files": [ { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1501, "image_height": 930, "name": "ulschmid-2025-apc-image.webp", "type": "image/webp", "size": 91268, "path": "Publication:ulschmid-2025-apc", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/ulschmid-2025-apc/ulschmid-2025-apc-image.webp", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/ulschmid-2025-apc/ulschmid-2025-apc-image:thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "ulschmid-2025-apc-paper.pdf", "type": "application/pdf", "size": 1866148, "path": "Publication:ulschmid-2025-apc", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/ulschmid-2025-apc/ulschmid-2025-apc-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/ulschmid-2025-apc/ulschmid-2025-apc-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "d4314", "d9275" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2025/ulschmid-2025-apc/", "__class": "Publication" }, { "id": "goel-2024-rdr", "type_id": "journalpaper", "tu_id": null, "repositum_id": "20.500.12708/204000", "title": "Real-Time Decompression and Rasterization of Massive Point Clouds", "date": "2024-08-09", "abstract": "Large-scale capturing of real-world scenes as 3D point clouds (e.g., using LIDAR scanning) generates billions of points that are challenging to visualize. High storage requirements prevent the quick and easy inspection of captured datasets on user-grade hardware. The fastest real-time rendering methods are limited by the available GPU memory and render only around 1 billion points interactively. We show that we can achieve state-of-the-art in both while simultaneously supporting datasets that surpass the capabilities of other methods. We present an on-the-fly point cloud decompression scheme that tightly integrates with software rasterization to reduce on-chip memory requirements by more than 4×. Our method compresses geometry losslessly and provides high visual quality at real-time framerates. We use a GPU-friendly, clipped Huffman encoding for compression. Point clouds are divided into equal-sized batches, which are Huffman-encoded independently. Batches are further subdivided to form easy-to-consume streams of data for massively parallel execution. The compressed point clouds are stored in an access-aware manner to achieve coherent GPU memory access and a high L1 cache hit rate at render time. Our approach can decompress and rasterize up to 120 million Huffman-encoded points per millisecond on-the-fly. We evaluate the quality and performance of our approach on various large datasets against the fastest competing methods. Our approach renders massive 3D point clouds at competitive frame rates and visual quality while consuming significantly less memory, thus unlocking unprecedented performance for the visualization of challenging datasets on commodity GPUs.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "cover", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 1695, "image_height": 961, "name": "goel-2024-rdr-cover.jpg", "type": "image/jpeg", "size": 276042, "path": "Publication:goel-2024-rdr", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/goel-2024-rdr/goel-2024-rdr-cover.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/goel-2024-rdr/goel-2024-rdr-cover:thumb{{size}}.png" }, "sync_repositum_override": "projects,date_from,date_to,event,lecturer,location", "repositum_presentation_id": null, "authors": [ 5402, 1116, 5403, 1650 ], "articleno": "48", "date_from": "2024-06-26", "date_to": "2024-06-28", "doi": "10.1145/3675373", "event": "High Performance Graphics", "issn": "2577-6193", "journal": "Proceedings of the ACM on Computer Graphics and Interactive Techniques", "lecturer": [ 1116 ], "location": "Denver, USA", "number": "3", "pages": "15", "pages_from": "1", "pages_to": "15", "publisher": "Association for Computing Machinery (ACM)", "volume": "7", "research_areas": [ "Rendering" ], "keywords": [ "compression", "point cloud", "rasterization", "real-time rendering" ], "weblinks": [], "files": [ { "description": null, "filetitle": "cover", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 1695, "image_height": 961, "name": "goel-2024-rdr-cover.jpg", "type": "image/jpeg", "size": 276042, "path": "Publication:goel-2024-rdr", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/goel-2024-rdr/goel-2024-rdr-cover.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/goel-2024-rdr/goel-2024-rdr-cover:thumb{{size}}.png" } ], "projects_workgroups": [ "rend", "d9275" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/goel-2024-rdr/", "__class": "Publication" }, { "id": "kerbl-2024-ah3", "type_id": "journalpaper", "tu_id": null, "repositum_id": "20.500.12708/220026", "title": "A Hierarchical 3D Gaussian Representation for Real-Time Rendering of Very Large Datasets", "date": "2024-07-19", "abstract": "Novel view synthesis has seen major advances in recent years, with 3D Gaussian splatting offering an excellent level of visual quality, fast training and real-time rendering. However, the resources needed for training and rendering inevitably limit the size of the captured scenes that can be represented with good visual quality. We introduce a hierarchy of 3D Gaussians that preserves visual quality for very large scenes, while offering an efficient Level-of-Detail (LOD) solution for efficient rendering of distant content with effective level selection and smooth transitions between levels. We introduce a divide-and-conquer approach that allows us to train very large scenes in independent chunks. We consolidate the chunks into a hierarchy that can be optimized to further improve visual quality of Gaussians merged into intermediate nodes. Very large captures typically have sparse coverage of the scene, presenting many challenges to the original 3D Gaussian splatting training method; we adapt and regularize training to account for these issues. We present a complete solution, that enables real-time rendering of very large scenes and can adapt to available resources thanks to our LOD method. We show results for captured scenes with up to tens of thousands of images with a simple and affordable rig, covering trajectories of up to several kilometers and lasting up to one hour.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": "date_from,date_to,event,lecturer,location", "repositum_presentation_id": null, "authors": [ 1650, 5532, 5372, 193, 5373, 5503 ], "articleno": "62", "date_from": "2024-07-28", "date_to": "2024-08-01", "doi": "10.1145/3658160", "event": "ACM SIGGRAPH 2024", "issn": "1557-7368", "journal": "ACM Transactions on Graphics", "lecturer": [ 5532 ], "location": "Denver, USA", "number": "4", "pages": "15", "pages_from": "1", "pages_to": "15", "publisher": "ASSOC COMPUTING MACHINERY", "volume": "43", "research_areas": [ "Rendering" ], "keywords": [ "real-time rendering", "3d gaussian splatting", "level-of-detail", "Large Scenes" ], "weblinks": [ { "href": "https://repo-sam.inria.fr/fungraph/hierarchical-3d-gaussians/", "caption": "project page", "description": null, "main_file": 0 } ], "files": [ { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "kerbl-2024-ah3-paper.pdf", "type": "application/pdf", "size": 10836570, "path": "Publication:kerbl-2024-ah3", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/kerbl-2024-ah3/kerbl-2024-ah3-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/kerbl-2024-ah3/kerbl-2024-ah3-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "d9275" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/kerbl-2024-ah3/", "__class": "Publication" }, { "id": "papantonakis-2024-rmf", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/198651", "title": "Reducing the Memory Footprint of 3D Gaussian Splatting", "date": "2024-05-13", "abstract": "3D Gaussian splatting provides excellent visual quality for novel view synthesis, with fast training and realtime rendering; unfortunately, the memory requirements of this method for storing and transmission are unreasonably high. We first analyze the reasons for this, identifying three main areas where storage can be reduced: the number of 3D Gaussian primitives used to represent a scene, the number of coefficients for the spherical harmonics used to represent directional radiance, and the precision required to store Gaussian primitive attributes. We present a solution to each of these issues. First, we propose an efficient, resolution-aware primitive pruning approach, reducing the primitive count by half. Second, we introduce an adaptive adjustment method to choose the number of coefficients used to represent directional radiance for each Gaussian primitive, and finally a codebook-based quantization method, together with a half-float representation for further memory reduction. Taken together, these three components result in a x27 reduction in overall size on disk on the standard datasets we tested, along with a x1.7 speedup in rendering speed. We demonstrate our method on standard datasets and show how our solution results in significantly reduced download times when using the method on a mobile device (see Fig. 1).", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": "projects", "repositum_presentation_id": null, "authors": [ 5371, 5372, 1650, 5373, 803 ], "articleno": "16", "doi": "10.1145/3651282", "issn": "2577-6193", "journal": "Proceedings of the ACM on Computer Graphics and Interactive Techniques", "number": "1", "pages": "17", "pages_from": "1", "pages_to": "17", "publisher": "Association for Computing Machinery (ACM)", "volume": "7", "research_areas": [], "keywords": [ "3D gaussian splatting", "memory reduction", "novel view synthesis", "radiance fields" ], "weblinks": [], "files": [], "projects_workgroups": [ "d9275", "rend" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/papantonakis-2024-rmf/", "__class": "Publication" }, { "id": "unterguggenberger-2024-fropo", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/199275", "title": "Fast Rendering of Parametric Objects on Modern GPUs", "date": "2024-05", "abstract": "Parametric functions are an extremely efficient representation for 3D geometry, capable of compactly modelling highly complex objects. Once specified, parametric 3D objects allow for visualization at arbitrary levels of detail, at no additional memory cost, limited only by the amount of evaluated samples. However, mapping the sample evaluation to the hardware rendering pipelines of modern graphics processing units (GPUs) is not trivial. This has given rise to several specialized solutions, each targeting interactive rendering of a constrained set of parametric functions. In this paper, we propose a general method for efficient rendering of parametrically defined 3D objects. Our solution is carefully designed around modern hardware architecture. Our method adaptively analyzes, allocates and evaluates parametric function samples to produce high-quality renderings. Geometric precision can be modulated from few pixels down to sub-pixel level, enabling real-time frame rates of several 100 frames per second (FPS) for various parametric functions. We propose a dedicated level-of-detail (LOD) stage, which outputs patches of similar geometric detail to a subsequent rendering stage that uses either a hardware tessellation-based approach or performs point-based softare rasterization. Our method requires neither preprocessing nor caching, and the proposed LOD mechanism is fast enough to run each frame. Hence, our approach also lends itself to animated parametric objects. We demonstrate the benefits of our method over a state-of-the-art spherical harmonics (SH) glyph rendering method, while showing its flexibility on a range of other demanding shapes.", "authors_et_al": false, "substitute": null, "main_image": { "description": "Teaser image, different parametric objects", "filetitle": "image", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 1440, "image_height": 1364, "name": "unterguggenberger-2024-fropo-image.png", "type": "image/png", "size": 2235242, "path": "Publication:unterguggenberger-2024-fropo", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/unterguggenberger-2024-fropo/unterguggenberger-2024-fropo-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/unterguggenberger-2024-fropo/unterguggenberger-2024-fropo-image:thumb{{size}}.png" }, "sync_repositum_override": "date,projects", "repositum_presentation_id": null, "authors": [ 848, 1525, 193, 1650, 1116 ], "booktitle": "EGPGV24: Eurographics Symposium on Parallel Graphics and Visualization", "date_from": "2024-05-27", "date_to": "2024-05-27", "doi": "10.2312/pgv.20241129", "event": "Eurographics Symposium on Parallel Graphics and Visualization (2024)", "isbn": "978-3-03868-243-1", "lecturer": [ 848 ], "location": "Odense", "open_access": "yes", "pages": "12", "publisher": "The Eurographics Association", "research_areas": [ "Rendering" ], "keywords": [ "Tessellation Shaders", "Point-Based Rendering", "Parametric Objects", "Fast Rendering", "Modern GPUs" ], "weblinks": [ { "href": "https://github.com/cg-tuwien/FastRenderingOfParametricObjects", "caption": "FastRenderingOfParametricObjects", "description": "Source Code on GitHub", "main_file": 0 } ], "files": [ { "description": "Teaser image, different parametric objects", "filetitle": "image", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 1440, "image_height": 1364, "name": "unterguggenberger-2024-fropo-image.png", "type": "image/png", "size": 2235242, "path": "Publication:unterguggenberger-2024-fropo", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/unterguggenberger-2024-fropo/unterguggenberger-2024-fropo-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/unterguggenberger-2024-fropo/unterguggenberger-2024-fropo-image:thumb{{size}}.png" }, { "description": "Published Paper", "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "unterguggenberger-2024-fropo-paper.pdf", "type": "application/pdf", "size": 17591180, "path": "Publication:unterguggenberger-2024-fropo", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/unterguggenberger-2024-fropo/unterguggenberger-2024-fropo-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/unterguggenberger-2024-fropo/unterguggenberger-2024-fropo-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "d9275", "rend" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/unterguggenberger-2024-fropo/", "__class": "Publication" }, { "id": "ulschmid-2024-reo", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/195927", "title": "Real-Time Editing of Path-Traced Scenes with Prioritized Re-Rendering", "date": "2024-02", "abstract": "With recent developments in GPU ray tracing performance and (AI-accelerated) noise reduction techniques, Monte Carlo Path Tracing at real-time rates becomes a viable solution for interactive 3D scene editing, with growing support in popular software. However, even for minor edits (e.g., adjusting materials or moving small objects), current solutions usually discard previous samples and the image formation process is started from scratch. In this paper, we present two adaptive, priority-based re-rendering techniques with incremental updates, prioritizing the reconstruction of regions with high importance, before gradually moving to less important regions. The suggested methods automatically identify and schedule sampling and accumulation of immediately affected regions. An extensive user study analyzes whether such prioritized renderings are beneficial to interactive scene editing, comparing them with same-time conventional re-rendering. Our evaluation shows that even with simple prio rity policies, there is a significant preference for such incremental rendering techniques for interactive editing of small objects over full-screen re-rendering with denoising.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 378, "image_height": 323, "name": "ulschmid-2024-reo-teaser.png", "type": "image/png", "size": 254543, "path": "Publication:ulschmid-2024-reo", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/ulschmid-2024-reo/ulschmid-2024-reo-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/ulschmid-2024-reo/ulschmid-2024-reo-teaser:thumb{{size}}.png" }, "sync_repositum_override": "booktitle", "repositum_presentation_id": null, "authors": [ 1954, 1650, 1030, 193 ], "booktitle": "Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - GRAPP and VISIGRAPP", "date_from": "2024-02-27", "date_to": "2024-02-29", "doi": "10.5220/0012324600003660", "editor": "Bashford-Rogers, Thomas and Meneveaux, Daniel and Ziat, Mounia and Ammi, Mehdi and Jänicke, Stefan and Purchase, Helen and Bouatouch, Kadi and Sousa, Augusto A.", "event": "19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications", "isbn": "978-989-758-679-8", "lecturer": [ 1954 ], "location": "Rom", "note": "Best student paper award", "pages": "12", "pages_from": "46", "pages_to": "57", "research_areas": [], "keywords": [ "ray tracing", "user study", "editing" ], "weblinks": [ { "href": "https://github.com/cg-tuwien/Prioritized-ReRendering", "caption": "GitHub", "description": null, "main_file": 1 } ], "files": [ { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "ulschmid-2024-reo-paper.pdf", "type": "application/pdf", "size": 14987716, "path": "Publication:ulschmid-2024-reo", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/ulschmid-2024-reo/ulschmid-2024-reo-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/ulschmid-2024-reo/ulschmid-2024-reo-paper:thumb{{size}}.png" }, { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 378, "image_height": 323, "name": "ulschmid-2024-reo-teaser.png", "type": "image/png", "size": 254543, "path": "Publication:ulschmid-2024-reo", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/ulschmid-2024-reo/ulschmid-2024-reo-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/ulschmid-2024-reo/ulschmid-2024-reo-teaser:thumb{{size}}.png" }, { "description": null, "filetitle": "video", "main_file": false, "use_in_gallery": false, "access": "public", "name": "ulschmid-2024-reo-video.mp4", "type": "video/mp4", "size": 38712872, "path": "Publication:ulschmid-2024-reo", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/ulschmid-2024-reo/ulschmid-2024-reo-video.mp4", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/ulschmid-2024-reo/ulschmid-2024-reo-video:thumb{{size}}.png", "video_mp4": "https://www.cg.tuwien.ac.at/research/publications/2024/ulschmid-2024-reo/ulschmid-2024-reo-video:video.mp4" } ], "projects_workgroups": [ "d9275", "d4314" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/ulschmid-2024-reo/", "__class": "Publication" }, { "id": "voglreiter-2023-tro", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/189596", "title": "Trim Regions for Online Computation of From-Region Potentially Visible Sets", "date": "2023-08", "abstract": "Visibility computation is a key element in computer graphics applications. More specifically, a from-region potentially visible set (PVS) is an established tool in rendering acceleration, but its high computational cost means a from-region PVS is almost always precomputed. Precomputation restricts the use of PVS to static scenes and leads to high storage cost, in particular, if we need fine-grained regions. For dynamic applications, such as streaming content over a variable-bandwidth network, online PVS computation with configurable region size is required. We address this need with trim regions, a new method for generating from-region PVS for arbitrary scenes in real time. Trim regions perform controlled erosion of object silhouettes in image space, implicitly applying the shrinking theorem known from previous work. Our algorithm is the first that applies automatic shrinking to unconstrained 3D scenes, including non-manifold meshes, and does so in real time using an efficient GPU execution model. We demonstrate that our algorithm generates a tight PVS for complex scenes and outperforms previous online methods for from-viewpoint and from-region PVS. It runs at 60 Hz for realistic game scenes consisting of millions of triangles and computes PVS with a tightness matching or surpassing existing approaches.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 302, "image_height": 167, "name": "voglreiter-2023-tro-teaser.jpg", "type": "image/jpeg", "size": 10478, "path": "Publication:voglreiter-2023-tro", "url": "https://www.cg.tuwien.ac.at/research/publications/2023/voglreiter-2023-tro/voglreiter-2023-tro-teaser.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2023/voglreiter-2023-tro/voglreiter-2023-tro-teaser:thumb{{size}}.png" }, "sync_repositum_override": "projects", "repositum_presentation_id": null, "authors": [ 5298, 1650, 5299, 5300, 5301, 1662, 202 ], "articleno": "85", "doi": "10.1145/3592434", "issn": "1557-7368", "journal": "ACM Transactions on Graphics", "number": "4", "pages": "15", "pages_from": "1", "pages_to": "15", "publisher": "ASSOC COMPUTING MACHINERY", "volume": "42", "research_areas": [], "keywords": [ "potentially visible sets", "real-time", "occlusion" ], "weblinks": [], "files": [ { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 302, "image_height": 167, "name": "voglreiter-2023-tro-teaser.jpg", "type": "image/jpeg", "size": 10478, "path": "Publication:voglreiter-2023-tro", "url": "https://www.cg.tuwien.ac.at/research/publications/2023/voglreiter-2023-tro/voglreiter-2023-tro-teaser.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2023/voglreiter-2023-tro/voglreiter-2023-tro-teaser:thumb{{size}}.png" } ], "projects_workgroups": [ "d9275", "rend" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2023/voglreiter-2023-tro/", "__class": "Publication" }, { "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, "repositum_presentation_id": null, "authors": [ 1116, 1650, 1802, 193 ], "articleno": "e14877", "date_from": "2023", "date_to": "2023", "doi": "10.1111/cgf.14877", "event": "High Performance Graphics", "issn": "1467-8659", "journal": "Computer Graphics Forum", "lecturer": [ 1116 ], "location": "Delft", "number": "8", "open_access": "yes", "pages": "12", "pages_from": "1", "pages_to": "12", "publisher": "WILEY", "volume": "42", "research_areas": [ "Rendering" ], "keywords": [ "point cloud rendering", "level of detail", "LOD" ], "weblinks": [ { "href": "https://github.com/m-schuetz/CudaLOD", "caption": "Source Code", "description": null, "main_file": 0 } ], "files": [ { "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" }, { "description": null, "filetitle": "paper", "main_file": false, "use_in_gallery": false, "access": "public", "name": "SCHUETZ-2023-LOD-paper.pdf", "type": "application/pdf", "size": 12634836, "path": "Publication:SCHUETZ-2023-LOD", "url": "https://www.cg.tuwien.ac.at/research/publications/2023/SCHUETZ-2023-LOD/SCHUETZ-2023-LOD-paper.pdf", "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-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "d4386" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2023/SCHUETZ-2023-LOD/", "__class": "Publication" }, { "id": "unterguggenberger-2023-vaw", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/175991", "title": "Vulkan all the way: Transitioning to a modern low-level graphics API in academia", "date": "2023-04", "abstract": "For over two decades, the OpenGL API provided users with the means for implementing versatile, feature-rich, and portable real-time graphics applications. Consequently, it has been widely adopted by practitioners and educators alike and is deeply ingrained in many curricula that teach real-time graphics for higher education. Over the years, the architecture of graphics processing units (GPUs) incrementally diverged from OpenGL's conceptual design. The more recently introduced Vulkan API provides a more modern, fine-grained approach for interfacing with the GPU, which allows a high level of controllability and, thereby, deep insights into the inner workings of modern GPUs. This property makes the Vulkan API especially well suitable for teaching graphics programming in university education, where fundamental knowledge shall be conveyed. Hence, it stands to reason that educators who have their students’ best interests at heart should provide them with corresponding lecture material. However, Vulkan is notoriously verbose and rather challenging for first-time users, thus transitioning to this new API bears a considerable risk of failing to achieve expected teaching goals. In this paper, we document our experiences after teaching Vulkan in both introductory and advanced graphics courses side-by-side with conventional OpenGL. A collection of surveys enables us to draw conclusions about perceived workload, difficulty, and students’ acceptance of either approach. In doing so, we identify suitable conditions and recommendations for teaching Vulkan to both undergraduate and graduate students.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1216, "image_height": 1216, "name": "unterguggenberger-2023-vaw-image.png", "type": "image/png", "size": 1536211, "path": "Publication:unterguggenberger-2023-vaw", "url": "https://www.cg.tuwien.ac.at/research/publications/2023/unterguggenberger-2023-vaw/unterguggenberger-2023-vaw-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2023/unterguggenberger-2023-vaw/unterguggenberger-2023-vaw-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 848, 1650, 193 ], "doi": "10.1016/j.cag.2023.02.001", "issn": "1873-7684", "journal": "Computers and Graphics", "open_access": "yes", "pages": "11", "pages_from": "155", "pages_to": "165", "publisher": "Elsevier", "volume": "111", "research_areas": [], "keywords": [ "GPU", "Graphics API", "Programming framework", "Real-time rendering", "Teaching", "Vulkan" ], "weblinks": [ { "href": "https://github.com/cg-tuwien/VulkanLaunchpad", "caption": "Vulkan Launchpad", "description": "A framework by TU Wien targeted at Vulkan beginners.", "main_file": 1 }, { "href": "https://github.com/cg-tuwien/Auto-Vk", "caption": "Auto-Vk", "description": "A low-level convenience and productivity layer for Vulkan", "main_file": 1 }, { "href": "https://github.com/cg-tuwien/Auto-Vk-Toolkit", "caption": "Auto-Vk-Toolkit", "description": "C++ framework atop Auto-Vk for rapid prototyping, research, and teaching.", "main_file": 1 }, { "href": "https://education.siggraph.org/cgsource/content/road-vulkan-teaching-vulkan-introductory-graphics-courses", "caption": "\"The Road to Vulkan\" at cgSource", "description": "Entry at the ACM SIGGRAPH Education Committee's cgSource repository.", "main_file": 0 } ], "files": [ { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1216, "image_height": 1216, "name": "unterguggenberger-2023-vaw-image.png", "type": "image/png", "size": 1536211, "path": "Publication:unterguggenberger-2023-vaw", "url": "https://www.cg.tuwien.ac.at/research/publications/2023/unterguggenberger-2023-vaw/unterguggenberger-2023-vaw-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2023/unterguggenberger-2023-vaw/unterguggenberger-2023-vaw-image:thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "unterguggenberger-2023-vaw-paper.pdf", "type": "application/pdf", "size": 972237, "path": "Publication:unterguggenberger-2023-vaw", "url": "https://www.cg.tuwien.ac.at/research/publications/2023/unterguggenberger-2023-vaw/unterguggenberger-2023-vaw-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2023/unterguggenberger-2023-vaw/unterguggenberger-2023-vaw-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "d4314" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2023/unterguggenberger-2023-vaw/", "__class": "Publication" }, { "id": "hladky-2022-QS", "type_id": "journalpaper", "tu_id": null, "repositum_id": "20.500.12708/152307", "title": "QuadStream: A Quad-Based Scene Streaming Architecture for Novel Viewpoint Reconstruction", "date": "2022-12", "abstract": "Cloud rendering is attractive when targeting thin client devices such as phones or VR/AR headsets, or any situation where a high-end GPU is not available due to thermal or power constraints. However, it introduces the challenge of streaming rendered data over a network in a manner that is robust to latency and potential dropouts. Current approaches range from streaming transmitted video and correcting it on the client---which fails in the presence of disocclusion events---to solutions where the server sends geometry and all rendering is performed on the client. To balance the competing goals of disocclusion robustness and minimal client workload, we introduce QuadStream, a new streaming technique that reduces motion-to-photon latency by allowing clients to render novel views on the fly and is robust against disocclusions. Our key idea is to transmit an approximate geometric scene representation to the client which is independent of the source geometry and can render both the current view frame and nearby adjacent views. Motivated by traditional macroblock approaches to video codec design, we decompose the scene seen from positions in a view cell into a series of view-aligned quads from multiple views, or QuadProxies. By operating on a rasterized G-Buffer, our approach is independent of the representation used for the scene itself. Our technical contributions are an efficient parallel quad generation, merging, and packing strategy for proxy views that cover potential client movement in a scene; a packing and encoding strategy allowing masked quads with depth information to be transmitted as a frame coherent stream; and an efficient rendering approach that takes advantage of modern hardware capabilities to turn our QuadStream representation into complete novel views on thin clients. According to our experiments, our approach achieves superior quality compared both to streaming methods that rely on simple video data and to geometry-based streaming.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 500, "image_height": 281, "name": "hladky-2022-QS-teaser.jpg", "type": "image/jpeg", "size": 176021, "path": "Publication:hladky-2022-QS", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/hladky-2022-QS/hladky-2022-QS-teaser.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/hladky-2022-QS/hladky-2022-QS-teaser:thumb{{size}}.png" }, "sync_repositum_override": "date,location,lecturer,event,date_to,date_from", "repositum_presentation_id": null, "authors": [ 5184, 5185, 5186, 1650, 312, 1662 ], "cfp": { "name": "cfp.pdf", "type": "application/pdf", "error": "0", "size": "737990", "orig_name": "cfp.pdf", "ext": "pdf" }, "date_from": "2022-12-06", "date_to": "2022-12-09", "event": "Siggraph Asia", "issn": "1557-7368", "journal": "ACM Transactions on Graphics", "lecturer": [ 5184 ], "location": "Daegu", "number": "6", "publisher": "ASSOC COMPUTING MACHINERY", "volume": "41", "research_areas": [ "Geometry", "Rendering", "VR" ], "keywords": [ "streaming", "real-time rendering", "virtual reality" ], "weblinks": [], "files": [ { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 500, "image_height": 281, "name": "hladky-2022-QS-teaser.jpg", "type": "image/jpeg", "size": 176021, "path": "Publication:hladky-2022-QS", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/hladky-2022-QS/hladky-2022-QS-teaser.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/hladky-2022-QS/hladky-2022-QS-teaser:thumb{{size}}.png" } ], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2022/hladky-2022-QS/", "__class": "Publication" }, { "id": "SCHUETZ-2022-PCC", "type_id": "journalpaper", "tu_id": null, "repositum_id": "20.500.12708/193255", "title": "Software Rasterization of 2 Billion Points in Real Time", "date": "2022-07", "abstract": "We propose a software rasterization pipeline for point clouds that is capable of brute-force rendering up to two billion points in real time (60fps). 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. ", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 516, "image_height": 368, "name": "SCHUETZ-2022-PCC-teaser.png", "type": "image/png", "size": 291356, "path": "Publication:SCHUETZ-2022-PCC", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/SCHUETZ-2022-PCC/SCHUETZ-2022-PCC-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/SCHUETZ-2022-PCC/SCHUETZ-2022-PCC-teaser:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1116, 1650, 193 ], "cfp": { "name": "cfp.pdf", "type": "application/pdf", "error": "0", "size": "1439654", "orig_name": "cfp.pdf", "ext": "pdf" }, "date_from": "2022-07-11", "date_to": "2022-07-14", "doi": "10.1145/3543863", "event": "High Performance Graphics 2022", "issn": "2577-6193", "journal": "Proceedings of the ACM on Computer Graphics and Interactive Techniques", "lecturer": [ 1116 ], "location": "Vancouver", "number": "3", "open_access": "yes", "pages": "17", "pages_from": "1", "pages_to": "17", "publisher": "Association for Computing Machinery (ACM)", "volume": "5", "research_areas": [ "Geometry", "Rendering" ], "keywords": [ "point-based rendering" ], "weblinks": [], "files": [ { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "SCHUETZ-2022-PCC-paper.pdf", "type": "application/pdf", "size": 7306416, "path": "Publication:SCHUETZ-2022-PCC", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/SCHUETZ-2022-PCC/SCHUETZ-2022-PCC-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/SCHUETZ-2022-PCC/SCHUETZ-2022-PCC-paper:thumb{{size}}.png" }, { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 516, "image_height": 368, "name": "SCHUETZ-2022-PCC-teaser.png", "type": "image/png", "size": 291356, "path": "Publication:SCHUETZ-2022-PCC", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/SCHUETZ-2022-PCC/SCHUETZ-2022-PCC-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/SCHUETZ-2022-PCC/SCHUETZ-2022-PCC-teaser:thumb{{size}}.png" } ], "projects_workgroups": [ "d4386" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2022/SCHUETZ-2022-PCC/", "__class": "Publication" }, { "id": "cardoso-2022-rtpercept", "type_id": "journalpaper", "tu_id": null, "repositum_id": "20.500.12708/142206", "title": "Training and Predicting Visual Error for Real-Time Applications", "date": "2022-05", "abstract": "Visual error metrics play a fundamental role in the quantification of perceived image similarity. Most recently, use cases for them in real-time applications have emerged, such as content-adaptive shading and shading reuse to increase performance and improve efficiency. A wide range of different metrics has been established, with the most sophisticated being capable of capturing the perceptual characteristics of the human visual system. However, their complexity, computational expense, and reliance on reference images to compare against prevent their generalized use in real-time, restricting such applications to using only the simplest available metrics.\n\nIn this work, we explore the abilities of convolutional neural networks to predict a variety of visual metrics without requiring either reference or rendered images. Specifically, we train and deploy a neural network to estimate the visual error resulting from reusing shading or using reduced shading rates. The resulting models account for 70%--90% of the variance while achieving up to an order of magnitude faster computation times. Our solution combines image-space information that is readily available in most state-of-the-art deferred shading pipelines with reprojection from previous frames to enable an adequate estimate of visual errors, even in previously unseen regions. We describe a suitable convolutional network architecture and considerations for data preparation for training. We demonstrate the capability of our network to predict complex error metrics at interactive rates in a real-time application that implements content-adaptive shading in a deferred pipeline. Depending on the portion of unseen image regions, our approach can achieve up to 2x performance compared to state-of-the-art methods.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1920, "image_height": 1088, "name": "cardoso-2022-rtpercept-teaser.png", "type": "image/png", "size": 2493649, "path": "Publication:cardoso-2022-rtpercept", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/cardoso-2022-rtpercept/cardoso-2022-rtpercept-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/cardoso-2022-rtpercept/cardoso-2022-rtpercept-teaser:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1639, 1650, 868, 1921, 193 ], "cfp": { "name": "cfp.pdf", "type": "application/pdf", "error": "0", "size": "1227280", "orig_name": "cfp.pdf", "ext": "pdf" }, "date_from": "2022-05-03", "date_to": "2022-05-05", "doi": "10.1145/3522625", "event": "ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games", "issn": "2577-6193", "journal": "Proceedings of the ACM on Computer Graphics and Interactive Techniques", "lecturer": [ 1639 ], "location": "online", "number": "1", "open_access": "yes", "pages": "17", "pages_from": "1", "pages_to": "17", "publisher": "Association for Computing Machinery", "volume": "5", "research_areas": [ "Perception", "Rendering" ], "keywords": [ "perceptual error", "variable rate shading", "real-time" ], "weblinks": [ { "href": "https://jaliborc.github.io/rt-percept/", "caption": "Paper Website", "description": null, "main_file": 1 } ], "files": [ { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "cardoso-2022-rtpercept-paper.pdf", "type": "application/pdf", "size": 54709850, "path": 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instead.\nThis fitting step also acts as a pooling layer if the number of Gaussian components is reduced appropriately.\nWe demonstrate that networks based on this architecture reach competitive accuracy on Gaussian mixtures fitted to the MNIST and ModelNet data sets.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 1500, "image_height": 1367, "name": "celarek-2022-gmcn-teaser.png", "type": "image/png", "size": 720960, "path": "Publication:celarek-2022-gmcn", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/celarek-2022-gmcn/celarek-2022-gmcn-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/celarek-2022-gmcn/celarek-2022-gmcn-teaser:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1013, 1919, 1650, 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Consequently, it has been widely adopted by practitioners and educators alike and is deeply ingrained in many curricula that teach real-time graphics for higher education. Over the years, the architecture of graphics processing units (GPUs) incrementally diverged from OpenGL's conceptual design. The more recently introduced Vulkan API provides a more modern, fine-grained approach for interfacing with the GPU. Various properties of this API and overall trends suggest that Vulkan could soon replace OpenGL in many areas. Hence, it stands to reason that educators who have their students' best interests at heart should provide them with corresponding lecture material. However, Vulkan is notoriously verbose and rather challenging for first-time users, thus transitioning to this new API bears a considerable risk of failing to achieve expected teaching goals. In this paper, we document our experiences after teaching Vulkan in an introductory graphics course side-by-side with conventional OpenGL. A final survey enables us to draw conclusions about perceived workload, difficulty, and students' acceptance of either approach and identify suitable conditions and recommendations for teaching Vulkan to undergraduate students.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1545, "image_height": 1041, "name": "unterguggenberger-2022-vulkan-teaser.png", "type": "image/png", "size": 1343937, "path": "Publication:unterguggenberger-2022-vulkan", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/unterguggenberger-2022-vulkan/unterguggenberger-2022-vulkan-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/unterguggenberger-2022-vulkan/unterguggenberger-2022-vulkan-teaser:thumb{{size}}.png" }, "sync_repositum_override": "date,projects,issn", "repositum_presentation_id": null, "authors": [ 848, 1650, 193 ], "booktitle": "Eurographics 2022 - Education Papers", "cfp": { "name": "cfp.pdf", "type": "application/pdf", "error": "0", "size": "566277", "orig_name": "cfp.pdf", "ext": "pdf" }, "date_from": "2022-04-25", "date_to": "2022-04-29", "doi": "10.2312/eged.20221043", "event": "Eurographics 2022", "isbn": "978-3-03868-170-0", "issn": "1017-4656", "lecturer": [ 848 ], "location": "Reims", "open_access": "yes", "pages": "9", "pages_from": "31", "pages_to": "39", "publisher": "The Eurographics Association", "research_areas": [ "Rendering" ], "keywords": [ "vulkan", "gpu", "opengl" ], "weblinks": [ { "href": "https://education.siggraph.org/cgsource/content/road-vulkan-teaching-vulkan-introductory-graphics-courses", "caption": "\"The Road to Vulkan\" at cgSource", "description": "Entry at the ACM SIGGRAPH Education Committee's cgSource repository.", "main_file": 0 }, { "href": "https://github.com/cg-tuwien/VulkanLaunchpad", "caption": "Vulkan Launchpad", "description": "A framework by TU Wien targeted at Vulkan beginners.", "main_file": 1 } ], "files": [ { "description": "The Road to Vulkan: Teaching Modern Low-Level APIs in Introductory Graphics Courses", "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "unterguggenberger-2022-vulkan-paper.pdf", "type": "application/pdf", "size": 497883, "path": "Publication:unterguggenberger-2022-vulkan", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/unterguggenberger-2022-vulkan/unterguggenberger-2022-vulkan-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/unterguggenberger-2022-vulkan/unterguggenberger-2022-vulkan-paper:thumb{{size}}.png" }, { "description": "The Road to Vulkan: Teaching Modern Low-Level APIs in Introductory Graphics Courses Slides", "filetitle": "slides", "main_file": false, "use_in_gallery": false, "access": "public", "name": "unterguggenberger-2022-vulkan-slides.pdf", "type": "application/pdf", "size": 3637778, "path": "Publication:unterguggenberger-2022-vulkan", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/unterguggenberger-2022-vulkan/unterguggenberger-2022-vulkan-slides.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/unterguggenberger-2022-vulkan/unterguggenberger-2022-vulkan-slides:thumb{{size}}.png" }, { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1545, "image_height": 1041, "name": "unterguggenberger-2022-vulkan-teaser.png", "type": "image/png", "size": 1343937, "path": "Publication:unterguggenberger-2022-vulkan", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/unterguggenberger-2022-vulkan/unterguggenberger-2022-vulkan-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/unterguggenberger-2022-vulkan/unterguggenberger-2022-vulkan-teaser:thumb{{size}}.png" } ], "projects_workgroups": [ "3DSpatialization" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2022/unterguggenberger-2022-vulkan/", "__class": "Publication" }, { "id": "kerbl-2022-trienc", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/139870", "title": "An Improved Triangle Encoding Scheme for Cached Tessellation", "date": "2022-04", "abstract": "With the recent advances in real-time rendering that were achieved by embracing software rasterization, the interest in alternative solutions for other fixed-function pipeline stages rises. In this paper, we revisit a recently presented software approach for cached tessellation, which compactly encodes and stores triangles in GPU memory. While the proposed technique is both efficient and versatile, we show that the original encoding is suboptimal and provide an alternative scheme that acts as a drop-in replacement. As shown in our evaluation, the proposed modifications can yield performance gains of 40\\% and more.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 1307, "image_height": 853, "name": "kerbl-2022-trienc-teaser.png", "type": "image/png", "size": 43599, "path": "Publication:kerbl-2022-trienc", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/kerbl-2022-trienc/kerbl-2022-trienc-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/kerbl-2022-trienc/kerbl-2022-trienc-teaser:thumb{{size}}.png" }, "sync_repositum_override": "date,event,pages,pages_from,pages_to,publisher,keywords", "repositum_presentation_id": null, "authors": [ 1650, 1772, 877, 193 ], "booktitle": "Eurographics 2022 - Short Papers", "cfp": { "name": "cfp.pdf", "type": "application/pdf", "error": "0", "size": "718298", "orig_name": "cfp.pdf", "ext": "pdf" }, "date_from": "2022-04-25", "date_to": "2022-04-29", "doi": "10.2312/egs.20221031", "editor": "Pelechano, Nuria and Vanderhaeghe, David", "event": "Eurographics 2022", "first_published": "2022", "isbn": "978-3-03868-169-4", "issn": "1017-4656", "lecturer": [ 1650 ], "location": "Reims", "open_access": "yes", "pages_from": "1", "pages_to": "4", "research_areas": [ "Geometry", "Rendering" ], "keywords": [ "gpu", "real-time", "tessellation" ], "weblinks": [], "files": [ { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "kerbl-2022-trienc-paper.pdf", "type": "application/pdf", "size": 2336032, "path": "Publication:kerbl-2022-trienc", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/kerbl-2022-trienc/kerbl-2022-trienc-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/kerbl-2022-trienc/kerbl-2022-trienc-paper:thumb{{size}}.png" }, { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 1307, "image_height": 853, "name": "kerbl-2022-trienc-teaser.png", "type": "image/png", "size": 43599, "path": "Publication:kerbl-2022-trienc", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/kerbl-2022-trienc/kerbl-2022-trienc-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/kerbl-2022-trienc/kerbl-2022-trienc-teaser:thumb{{size}}.png" } ], "projects_workgroups": [ "3DSpatialization" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2022/kerbl-2022-trienc/", "__class": "Publication" }, { "id": "kerbl-2022-cuda", "type_id": "otherreviewed", "tu_id": null, "repositum_id": null, "title": "CUDA and Applications to Task-based Programming", "date": "2022-04", "abstract": "To provide a profound understanding of how CUDA applications can achieve peak performance, the first two parts of this tutorial outline the modern CUDA architecture. Following a basic introduction, we expose how language features are linked to---and constrained by---the underlying physical hardware components. Furthermore, we describe common applications for massively parallel programming, offer a detailed breakdown of potential issues, and list ways to mitigate performance impacts. An exemplary analysis of PTX and SASS snippets illustrates how code patterns in CUDA are mapped to actual hardware instructions.\n\nIn parts 3 and 4, we focus on novel features that were enabled by the arrival of CUDA 10+ toolkits and the Volta+ architectures, such as ITS, tensor cores, and the graph API. In addition to basic use case demonstrations, we outline our own experiences with these capabilities and their potential performance benefits. We also discuss how long-standing best practices are affected by these changes and describe common caveats for dealing with legacy code on recent GPU models. We show how these considerations can be implemented in practice by presenting state-of-the-art research into task-based GPU scheduling, and how the dynamic adjustment of thread roles and group configurations can significantly increase performance.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 1800, "image_height": 1151, "name": "kerbl-2022-cuda-teaser.png", "type": "image/png", "size": 718260, "path": "Publication:kerbl-2022-cuda", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/kerbl-2022-cuda/kerbl-2022-cuda-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/kerbl-2022-cuda/kerbl-2022-cuda-teaser:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1650, 1661, 1834, 1662 ], "booktitle": "Eurographics 2022 - Tutorials", "editor": "Stefanie Hahmann and Gustavo Patow", "location": "Reims", "publisher": "The Eurographics Association", "research_areas": [], "keywords": [ "Parallel Programming", "GPU" ], "weblinks": [ { "href": "https://cuda-tutorial.github.io/index.html", "caption": "Tutorial Homepage", "description": "Course notes and code samples for the tutorial", "main_file": 0 } ], "files": [ { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "kerbl-2022-cuda-paper.pdf", "type": "application/pdf", "size": 798232, "path": "Publication:kerbl-2022-cuda", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/kerbl-2022-cuda/kerbl-2022-cuda-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/kerbl-2022-cuda/kerbl-2022-cuda-paper:thumb{{size}}.png" }, { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 1800, "image_height": 1151, "name": "kerbl-2022-cuda-teaser.png", "type": "image/png", "size": 718260, "path": "Publication:kerbl-2022-cuda", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/kerbl-2022-cuda/kerbl-2022-cuda-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/kerbl-2022-cuda/kerbl-2022-cuda-teaser:thumb{{size}}.png" } ], "projects_workgroups": [ "3DSpatialization" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2022/kerbl-2022-cuda/", "__class": "Publication" }, { "id": "roth_vdi", "type_id": "inproceedings", "tu_id": 300372, "repositum_id": "20.500.12708/55640", "title": "View-Dependent Impostors for Architectural Shape Grammars", "date": "2021-10", "abstract": "Procedural generation has become a key component in satisfying a growing demand for ever-larger, highly detailed geometry in realistic, open-world games and simulations. In this paper, we present our work towards a new level-of-detail mechanism for procedural geometry shape grammars. Our approach automatically identifies and adds suitable surrogate rules to a shape grammar's derivation tree. Opportunities for surrogates are detected in a dedicated pre-processing stage. Where suitable, textured impostors are then used for rendering based on the current viewpoint at runtime. Our proposed methods generate simplified geometry with superior visual quality to the state-of-the-art and roughly the same rendering performance.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 1920, "image_height": 1920, "name": "roth_vdi-teaser.png", "type": "image/png", "size": 6440142, "path": "Publication:roth_vdi", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/roth_vdi/roth_vdi-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/roth_vdi/roth_vdi-teaser:thumb{{size}}.png" }, "sync_repositum_override": "date", "repositum_presentation_id": null, "authors": [ 1558, 1847, 1650, 193 ], "booktitle": "Pacific Graphics Short Papers, Posters, and Work-in-Progress Papers", "cfp": { "name": "cfp.pdf", "type": "application/pdf", "error": "0", "size": "982263", "orig_name": "cfp.pdf", "ext": "pdf" }, "date_from": "2021-10-18", "date_to": "2021-10-21", "doi": "10.2312/pg.20211390", "editor": "Lee, Sung-Hee and Zollmann, Stefanie and Okabe, Makoto and Wünsche, Burkhard", "event": "Pacific Graphics 2021", "isbn": "978-3-03868-162-5", "lecturer": [ 1558 ], "location": "online", "open_access": "yes", "organization": "The Eurographics Association", "pages": "2", "pages_from": "63", "pages_to": "64", "publisher": "Eurographics Association", "research_areas": [ "Geometry", "Rendering" ], "keywords": [ "procedural geometry", "real-time", "GPU" ], "weblinks": [], "files": [ { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "roth_vdi-paper.pdf", "type": "application/pdf", "size": 3651426, "path": "Publication:roth_vdi", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/roth_vdi/roth_vdi-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/roth_vdi/roth_vdi-paper:thumb{{size}}.png" }, { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 1920, "image_height": 1920, "name": "roth_vdi-teaser.png", "type": "image/png", "size": 6440142, "path": "Publication:roth_vdi", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/roth_vdi/roth_vdi-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/roth_vdi/roth_vdi-teaser:thumb{{size}}.png" } ], "projects_workgroups": [ "3DSpatialization" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2021/roth_vdi/", "__class": "Publication" }, { "id": "murturi_PGG", "type_id": "inproceedings", "tu_id": 298543, "repositum_id": "20.500.12708/58547", "title": "On Provisioning Procedural Geometry Workloads on Edge Architectures", "date": "2021-10", "abstract": "Contemporary applications such as those within Augmented or Virtual Reality (AR/VR) pose challenges for software architectures supporting them, which have to adhere to stringent latency, data transmission, and performance requirements. This manifests in processing 3D models, whose 3D contents are increasingly generated procedurally rather than explicitly, resulting in computational workloads (i.e., perceived as Procedural Geometry Workloads) with particular characteristics and resource requirements. Traditionally, executing such workloads takes place in resource-rich environments such as the cloud. However, the massive amount of data transfer, heterogeneous devices, and networks involved affect latency, which in turn causes low-quality visualization in user-facing applications (e.g., AR/VR). To overcome such challenges, processing elements available close to end users can be leveraged to generate 3D models instead, and as such the edge emerges as a central architectural entity. This paper describes such procedural geometry workloads, their particular characteristics, and challenges to execute them on heterogeneous devices. Furthermore, we propose an architecture capable of provisioning procedural geometry workloads in edge scenarios.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "teaser2", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 2189, "image_height": 1487, "name": "murturi_PGG-teaser2.png", "type": "image/png", "size": 360252, "path": "Publication:murturi_PGG", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/murturi_PGG/murturi_PGG-teaser2.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/murturi_PGG/murturi_PGG-teaser2:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1708, 1558, 1650, 193, 1709, 1706 ], "booktitle": "Proceedings of the 17th International Conference on Web Information Systems and Technologies - WEBIST", "cfp": { "name": "cfp.pdf", "type": "application/pdf", "error": "0", "size": "2105613", "orig_name": "cfp.pdf", "ext": "pdf" }, "date_from": "2021-10-26", "date_to": "2021-10-28", "doi": "10.5220/0010687800003058", "editor": " Francisco Domínguez Mayo, Massimo Marchiori and Joaquim Filipe", "event": "17th International Conference on Web Information Systems and Technologies - WEBIST", "isbn": "978-989-758-536-4", "lecturer": [ 1708 ], "organization": "INSTICC", "pages": "6", "pages_from": "354", "pages_to": "359", "publisher": "SciTePress", "research_areas": [ "Geometry", "Rendering" ], "keywords": [ "distributed systems", "procedural geometry", "rendering" ], "weblinks": [], "files": [ { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "murturi_PGG-paper.pdf", "type": "application/pdf", "size": 3799321, "path": "Publication:murturi_PGG", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/murturi_PGG/murturi_PGG-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/murturi_PGG/murturi_PGG-paper:thumb{{size}}.png" }, { "description": null, "filetitle": "teaser2", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 2189, "image_height": 1487, "name": "murturi_PGG-teaser2.png", "type": "image/png", "size": 360252, "path": "Publication:murturi_PGG", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/murturi_PGG/murturi_PGG-teaser2.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/murturi_PGG/murturi_PGG-teaser2:thumb{{size}}.png" } ], "projects_workgroups": [ "3DSpatialization" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2021/murturi_PGG/", "__class": "Publication" }, { "id": "stappen_SteFAS", "type_id": "inproceedings", "tu_id": 300417, "repositum_id": "20.500.12708/58631", "title": "Temporally Stable Content-Adaptive and Spatio-Temporal Shading Rate Assignment for Real-Time Applications", "date": "2021-10", "abstract": "We propose two novel methods to improve the efficiency and quality of real-time rendering applications: Texel differential-based content-adaptive shading (TDCAS) and spatio-temporally filtered adaptive shading (STeFAS). Utilizing Variable Rate Shading (VRS)-a hardware feature introduced with NVIDIA's Turing micro-architecture-and properties derived during rendering or Temporal Anti-Aliasing (TAA), our techniques adapt the resolution to improve the performance and quality of real-time applications. VRS enables different shading resolution for different regions of the screen during a single render pass. In contrast to other techniques, TDCAS and STeFAS have very little overhead for computing the shading rate. STeFAS enables up to 4x higher rendering resolutions for similar frame rates, or a performance increase of 4× at the same resolution.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 1227, "image_height": 799, "name": "stappen_SteFAS-teaser.png", "type": "image/png", "size": 126643, "path": "Publication:stappen_SteFAS", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/stappen_SteFAS/stappen_SteFAS-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/stappen_SteFAS/stappen_SteFAS-teaser:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1406, 848, 1650, 193 ], "booktitle": "Pacific Graphics Short Papers, Posters, and Work-in-Progress Papers", "cfp": { "name": "cfp.pdf", "type": "application/pdf", "error": "0", "size": "982263", "orig_name": "cfp.pdf", "ext": "pdf" }, "date_from": "2021-10-18", "date_to": "2021-10-21", "doi": "10.2312/pg.20211391", "editor": "Lee, Sung-Hee and Zollmann, Stefanie and Okabe, Makoto and Wünsche, Burkhard", "event": "Pacific Graphics 2021", "isbn": "978-3-03868-162-5", "lecturer": [ 1406 ], "location": "online", "organization": "The Eurographics Association", "pages": "2", "pages_from": "65", "pages_to": "66", "publisher": "Eurographics Association", "research_areas": [ "Perception", "Rendering" ], "keywords": [ "variable rate shading", "temporal antialiasing" ], "weblinks": [], "files": [ { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "stappen_SteFAS-paper.pdf", "type": "application/pdf", "size": 173697, "path": "Publication:stappen_SteFAS", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/stappen_SteFAS/stappen_SteFAS-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/stappen_SteFAS/stappen_SteFAS-paper:thumb{{size}}.png" }, { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 1227, "image_height": 799, "name": "stappen_SteFAS-teaser.png", "type": "image/png", "size": 126643, "path": "Publication:stappen_SteFAS", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/stappen_SteFAS/stappen_SteFAS-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/stappen_SteFAS/stappen_SteFAS-teaser:thumb{{size}}.png" } ], "projects_workgroups": [ "3DSpatialization" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2021/stappen_SteFAS/", "__class": "Publication" }, { "id": "unterguggenberger-2021-msh", "type_id": "journalpaper", "tu_id": 300418, "repositum_id": "20.500.12708/58632", "title": "Conservative Meshlet Bounds for Robust Culling of Skinned Meshes", "date": "2021-10", "abstract": "Following recent advances in GPU hardware development and newly introduced rendering pipeline extensions, the segmentation of input geometry into small geometry clusters-so-called meshlets-has emerged as an important practice for efficient rendering of complex 3D models. Meshlets can be processed efficiently using mesh shaders on modern graphics processing units, in order to achieve streamlined geometry processing in just two tightly coupled shader stages that allow for dynamic workload manipulation in-between. The additional granularity layer between entire models and individual triangles enables new opportunities for fine-grained visibility culling methods. However, in contrast to static models, view frustum and backface culling on a per-meshlet basis for skinned, animated models are difficult to achieve while respecting the conservative spatio-temporal bounds that are required for robust rendering results. In this paper, we describe a solution for computing and exploiting relevant conservative bounds for culling meshlets of models that are animated using linear blend skinning. By enabling visibility culling for animated meshlets, our approach can help to improve rendering performance and alleviate bottlenecks in the notoriously performanceand memory-intensive skeletal animation pipelines of modern real-time graphics applications.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 849, "image_height": 653, "name": "unterguggenberger-2021-msh-teaser.png", "type": "image/png", "size": 990337, "path": "Publication:unterguggenberger-2021-msh", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/unterguggenberger-2021-msh/unterguggenberger-2021-msh-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/unterguggenberger-2021-msh/unterguggenberger-2021-msh-teaser:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 848, 1650, 1795, 193 ], "booktitle": "Computer Graphics Forum", "cfp": { "name": "cfp.pdf", "type": "application/pdf", "error": "0", "size": "982263", "orig_name": "cfp.pdf", "ext": "pdf" }, "date_from": "2021-10-18", "date_to": "2021-10-21", "doi": "10.1111/cgf.14401", "event": "Pacific Graphics 2021", "issn": "1467-8659", "journal": "Computer Graphics Forum", "lecturer": [ 848 ], "location": "online", "number": "7", "open_access": "yes", "pages": "13", "pages_from": "57", "pages_to": "69", "publisher": "Eurographics Association", "volume": "40", "research_areas": [ "Geometry", "Rendering" ], "keywords": [ "real-time rendering", "meshlet", "mesh shader", "task shader", "view frustum culling", "backface culling", "Vulkan", "vertex skinning", "animation", "conservative bounds", "bounding boxes", "Rodrigues' rotation formula", "spatio-temporal bounds" ], "weblinks": [], "files": [ { "description": "Conservative Meshlet Bounds for Robust Culling of Skinned Meshes", "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "unterguggenberger-2021-msh-paper.pdf", "type": "application/pdf", "size": 13842539, "path": "Publication:unterguggenberger-2021-msh", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/unterguggenberger-2021-msh/unterguggenberger-2021-msh-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/unterguggenberger-2021-msh/unterguggenberger-2021-msh-paper:thumb{{size}}.png" }, { "description": "Slides used for the talk", "filetitle": "slides", "main_file": false, "use_in_gallery": false, "access": "public", "name": "unterguggenberger-2021-msh-slides.pdf", "type": "application/pdf", "size": 4963013, "path": "Publication:unterguggenberger-2021-msh", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/unterguggenberger-2021-msh/unterguggenberger-2021-msh-slides.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/unterguggenberger-2021-msh/unterguggenberger-2021-msh-slides:thumb{{size}}.png" }, { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 849, "image_height": 653, "name": "unterguggenberger-2021-msh-teaser.png", "type": "image/png", "size": 990337, "path": "Publication:unterguggenberger-2021-msh", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/unterguggenberger-2021-msh/unterguggenberger-2021-msh-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/unterguggenberger-2021-msh/unterguggenberger-2021-msh-teaser:thumb{{size}}.png" } ], "projects_workgroups": [ "3DSpatialization" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2021/unterguggenberger-2021-msh/", "__class": "Publication" }, { "id": "kerbl_2021_hdg", "type_id": "talk", "tu_id": null, "repositum_id": null, "title": "Providing Highly Detailed Geometry for Cloud and Edge Real-Time Rendering", "date": "2021-07-07", "abstract": "Mesh shading was recently introduced as a topical GPU feature in the NVIDIA Turing and AMD RDNA2 GPU architectures, offering an alternative pathway for executing the transformation, generation and augmentation of geometry for hardware rasterization. Future trends in game development will rely on mesh shading and “meshlets”, using highly detailed meshes with deep level of detail hierarchies. Particularly powerful applications of meshlets include arbtirary culling and subdivision methods. Furthermore, advanced pre-computation include visibility and lighting information that can be stored on a per-meshlet basis, thus promoting the compression of attributes through quantization and the acceleration of computations via hierarchical processing.\n\nAlthough meshlets can be comprised from arbitrary assemblages of primitives, their benefits are highest when meshlet formation is done in a way that already takes the usecase into account. Individual formation procedures can be defined in order to achieve specific goals. As an example, we may generate meshlets that are optimized for global illumination techniques, by minimizing their curvature and variance in material coefficients. Incoming light can then be ray-traced and cached per meshlet, along with view-dependent variance encoded in a discretized data structure. More uniform meshlets thus require less data transferred for accurately approximating their global illumination, reducing the consumption of critical memory bandwidth. We may also partition entire scenes into meshlets that foster fast visibility culling for large groups of primitives, without transforming even a single vertex. In fact, meshlet formation policies can leverage arbitrary attributes, such as the distribution of UV coordinates, ambient occlusion or mesh topology in order to optimize them according to desired runtime criteria. Cloud gaming offers a unique opportunity for leveraging this technology at a larger scale: dedicated data storages and servers can maintain multiple copies of complex triangle meshes, each partitioned by a particular meshlet formation policy. A live monitor can react to a specific bottleneck by dynamically switching meshlets to best accommodate the current GPU resource requirements. In this talk, we will present the various possibilities for real-time rendering to benefit from mesh shading by means of optimized meshlet formation procedures.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 1037, "image_height": 985, "name": "kerbl_2021_hdg-teaser.png", "type": "image/png", "size": 330411, "path": "Publication:kerbl_2021_hdg", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/kerbl_2021_hdg/kerbl_2021_hdg-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/kerbl_2021_hdg/kerbl_2021_hdg-teaser:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1650 ], "event": "InnovWave 2021", "location": "online", "open_access": "no", "research_areas": [ "Rendering" ], "keywords": [ "cloud", "real-time", "rendering" ], "weblinks": [], "files": [ { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 1037, "image_height": 985, "name": "kerbl_2021_hdg-teaser.png", "type": "image/png", "size": 330411, "path": "Publication:kerbl_2021_hdg", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/kerbl_2021_hdg/kerbl_2021_hdg-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/kerbl_2021_hdg/kerbl_2021_hdg-teaser:thumb{{size}}.png" } ], "projects_workgroups": [ "3DSpatialization" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2021/kerbl_2021_hdg/", "__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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In this work, we present an alternative global illumination method: The stochastic substitute tree (SST), a hierarchical structure inspired by lightcuts with light probability distributions as inner nodes. Our approach distributes virtual point lights (VPLs) in every frame and efficiently constructs the SST over those lights by clustering according to Morton codes. Global illumination is approximated by sampling the SST and considers the BRDF at the hit location as well as the SST nodes’ intensities for importance sampling directly from inner nodes of the tree. To remove the introduced Monte Carlo noise, we use a recurrent autoencoder. 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Generating more than one view multiplies the amount of rendered geometry, which can cause a huge performance impact. Minimizing that impact has been a target of previous research and GPU manufacturers, who have started to equip devices with dedicated acceleration units. However, vendor-specific acceleration is not the only option to increase multi-view rendering (MVR) performance. Available graphics API features, shader stages and optimizations can be exploited for improved MVR performance, while generally offering more versatile pipeline configurations, including the preservation of custom tessellation and geometry shaders. In this paper, we present an exhaustive evaluation of MVR pipelines available on modern GPUs. We provide a detailed analysis of previous\ntechniques, hardware-accelerated MVR and propose a novel method, leading to the creation of an MVR catalogue. Our analyses cover three distinct applications to help gain clarity on overall MVR performance characteristics. 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Our approach to terrain and ocean mesh generation relies on a projected, persistent grid that can instantaneously and smoothly adapt to fast-changing viewpoints. For generating planetary ocean surfaces, we present a wave function that creates seamless, evenly spaced waves across the entire planet without causing unsightly artifacts. We further show how to render volumetric clouds in combination with precomputed atmospheric scattering and account for their contribution to light transport above ground. Our method provides mathematically consistent approximations of cloud-atmosphere interactions and works for any view point and direction, ensuring continuous transitions in appearance as the viewer moves from ground to space. Among others, our approach supports cloud shadows, light shafts, ocean reflections, and earth shadows on the clouds. 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