[ { "id": "schuetz-2025-splatshop", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/216694", "title": "Splatshop: Efficiently Editing Large Gaussian Splat Models", "date": "2025-06", "abstract": "We present Splatshop, a highly optimized toolbox for interactive editing (selection, deletion, painting, transformation, . . . ) of 3D Gaussian Splatting models. Utilizing a comprehensive collection of heuristic approaches, we carefully balance between exact and fast rendering to enable precise editing without sacrificing real-time performance. Our experiments confirm that Splatshop achieves these goals for scenes with up to 100 million primitives. We also show how our proposed pipeline can be extended for use with head-mounted displays. As such, Splatshop is the first VR-capable editor for large-scale 3D Gaussian Splatting models and a step towards a ''Photoshop for Gaussian Splatting.''", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "cover", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 1276, "image_height": 691, "name": "schuetz-2025-splatshop-cover.jpg", "type": "image/jpeg", "size": 327472, "path": "Publication:schuetz-2025-splatshop", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/schuetz-2025-splatshop/schuetz-2025-splatshop-cover.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/schuetz-2025-splatshop/schuetz-2025-splatshop-cover:thumb{{size}}.png" }, "sync_repositum_override": "date,projects,articleno", "repositum_presentation_id": null, "authors": [ 1116, 5492, 5493, 823, 842, 193 ], "articleno": "8", "doi": "10.1111/cgf.70214", "issn": "1467-8659", "journal": "Computer Graphics Forum", "note": "https://github.com/m-schuetz/Splatshop", "number": "8", "open_access": "yes", "pages": "12", "publisher": "WILEY", "volume": "44", "research_areas": [ "Rendering" ], "keywords": [ "gaussian splats", "point clouds", "real-time rendering" ], "weblinks": [], "files": [ { "description": null, "filetitle": "cover", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 1276, "image_height": 691, "name": "schuetz-2025-splatshop-cover.jpg", "type": "image/jpeg", "size": 327472, "path": "Publication:schuetz-2025-splatshop", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/schuetz-2025-splatshop/schuetz-2025-splatshop-cover.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/schuetz-2025-splatshop/schuetz-2025-splatshop-cover:thumb{{size}}.png" } ], "projects_workgroups": [ "d9275", "rend" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2025/schuetz-2025-splatshop/", "__class": "Publication" }, { "id": "erler-2025-lidarscout", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/216864", "title": "LidarScout: Direct Out-of-Core Rendering of Massive Point Clouds", "date": "2025-06", "abstract": "Large-scale terrain scans are the basis for many important tasks, such as topographic mapping, forestry, agriculture, and infrastructure planning. The resulting point cloud data sets are so massive in size that even basic tasks like viewing take hours to days of pre-processing in order to create level-of-detail structures that allow inspecting the data set in their entirety in real time. In this paper, we propose a method that is capable of instantly visualizing massive country-sized scans with hundreds of billions of points. Upon opening the data set, we first load a sparse subsample of points and initialize an overview of the entire point cloud, immediately followed by a surface reconstruction process to generate higher-quality, hole-free heightmaps. As users start navigating towards a region of interest, we continue to prioritize the heightmap construction process to the user's viewpoint. Once a user zooms in closely, we load the full-resolution point cloud data for that region and update the corresponding height map textures with the full-resolution data. As users navigate elsewhere, full-resolution point data that is no longer needed is unloaded, but the updated heightmap textures are retained as a form of medium level of detail. Overall, our method constitutes a form of direct out-of-core rendering for massive point cloud data sets (terabytes, compressed) that requires no preprocessing and no additional disk space. Source code, executable, pre-trained model, and dataset are available at: https://github.com/cg-tuwien/lidarscout", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 2456, "image_height": 1411, "name": "erler-2025-lidarscout-teaser.jpg", "type": "image/jpeg", "size": 421958, "path": "Publication:erler-2025-lidarscout", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/erler-2025-lidarscout/erler-2025-lidarscout-teaser.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/erler-2025-lidarscout/erler-2025-lidarscout-teaser:thumb{{size}}.png" }, "sync_repositum_override": "date,projects,articleno", "repositum_presentation_id": null, "authors": [ 1395, 1857, 193, 1116 ], "articleno": "10.2312/hpg.20251170", "booktitle": "High-Performance Graphics 2025 - Symposium Papers", "date_from": "2025-06-23", "date_to": "2025-06-25", "doi": "10.2312/hpg.20251170", "event": "High-Performance Graphics 2025", "isbn": "978-3-03868-291-2", "lecturer": [ 1395 ], "location": "Koppenhagen", "pages": "11", "publisher": "the Eurographics Association", "research_areas": [ "Geometry", "Modeling" ], "keywords": [ "Point-based models", "Mesh models", "Neural networks", "Reconstruction", "Aerial LIDAR" ], "weblinks": [ { "href": "https://github.com/cg-tuwien/lidarscout", "caption": "Repository of Viewer Source", "description": null, "main_file": 1 }, { "href": "https://github.com/cg-tuwien/lidarscout_training", "caption": "Repository of Training Source", "description": null, "main_file": 1 }, { "href": "https://www.youtube.com/watch?v=q7Ap-FHZufY&list=PLD5wBo3-8n8LVGp6BDOFuPgGANdoYg_uk&index=8", "caption": "Presentation from HPG 2025", "description": null, "main_file": 1 }, { "href": "https://doi.org/10.48436/443tr-j4096", "caption": "TUW Research Data Repository", 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"https://www.cg.tuwien.ac.at/research/publications/2025/erler-2025-lidarscout/erler-2025-lidarscout-teaser_large.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/erler-2025-lidarscout/erler-2025-lidarscout-teaser_large:thumb{{size}}.png" }, { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 2456, "image_height": 1411, "name": "erler-2025-lidarscout-teaser.jpg", "type": "image/jpeg", "size": 421958, "path": "Publication:erler-2025-lidarscout", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/erler-2025-lidarscout/erler-2025-lidarscout-teaser.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/erler-2025-lidarscout/erler-2025-lidarscout-teaser:thumb{{size}}.png" }, { "description": "A screen capture of the viewer", "filetitle": "video", "main_file": true, "use_in_gallery": false, "access": "public", "name": "erler-2025-lidarscout-video.mp4", "type": "video/mp4", "size": 122979998, "path": "Publication:erler-2025-lidarscout", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/erler-2025-lidarscout/erler-2025-lidarscout-video.mp4", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/erler-2025-lidarscout/erler-2025-lidarscout-video:thumb{{size}}.png", "video_mp4": "https://www.cg.tuwien.ac.at/research/publications/2025/erler-2025-lidarscout/erler-2025-lidarscout-video:video.mp4" }, { "description": "LidarScout Executable for Windows", "filetitle": "Viewer", "main_file": true, "use_in_gallery": false, "access": "public", "name": "erler-2025-lidarscout-Viewer.zip", "type": "application/x-zip-compressed", "size": 2360647751, "path": "Publication:erler-2025-lidarscout", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/erler-2025-lidarscout/erler-2025-lidarscout-Viewer.zip", "thumb_image_sizes": [] } ], "projects_workgroups": [ "d9275", "rend" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2025/erler-2025-lidarscout/", "__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": "SCHUETZ-2024-SIMLOD", "type_id": "journalpaper", "tu_id": null, "repositum_id": "20.500.12708/208716", "title": "SimLOD: Simultaneous LOD Generation and Rendering for Point Clouds", "date": "2024-05-13", "abstract": "LOD construction is typically implemented as a preprocessing step that requires users to wait before they are able to view the results in real time. We propose an incremental LOD generation approach for point clouds that allows us to simultaneously load points from disk, update an octree-based level-of-detail representation, and render the intermediate results in real time while additional points are still being loaded from disk. LOD construction and rendering are both implemented in CUDA and share the GPU’s processing power, but each incremental update is lightweight enough to leave enough time to maintain real-time frame rates. Our approach is able to stream points from an SSD and update the octree on the GPU at rates of up to 580 million points per second (~9.3GB/s) on an RTX 4090 and a PCIe 5.0 SSD. Depending on the data set, our approach spends an average of about 1 to 2 ms to incrementally insert 1 million points into the octree, allowing us to insert several million points per frame into the LOD structure and render the intermediate results within the same frame.", "authors_et_al": false, "substitute": null, "main_image": { "description": "Rendered Point Cloud to the left and points/voxels colored by the containing octree node to the right.", "filetitle": "SimLOD", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1911, "image_height": 1072, "name": "SCHUETZ-2024-SIMLOD-SimLOD.jpg", "type": "image/jpeg", "size": 323598, "path": "Publication:SCHUETZ-2024-SIMLOD", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/SCHUETZ-2024-SIMLOD/SCHUETZ-2024-SIMLOD-SimLOD.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/SCHUETZ-2024-SIMLOD/SCHUETZ-2024-SIMLOD-SimLOD:thumb{{size}}.png" }, "sync_repositum_override": "event", "repositum_presentation_id": null, "authors": [ 1116, 1857, 193 ], "articleno": "17", "date_from": "2023-05", "date_to": "2023-05", "doi": "10.1145/3651287", "event": "ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games 2024", "issn": "2577-6193", "journal": "Proceedings of the ACM on Computer Graphics and Interactive Techniques", "lecturer": [ 1116 ], "note": "Source Code: https://github.com/m-schuetz/SimLOD", "number": "1", "open_access": "yes", "pages": "20", "publisher": "Association for Computing Machinery (ACM)", "volume": "7", "research_areas": [ "Rendering" ], "keywords": [ "LOD", "real-time rendering", "Point Cloud Rendering", "rasterization", "Octree" ], "weblinks": [], "files": [ { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "SCHUETZ-2024-SIMLOD-paper.pdf", "type": "application/pdf", "size": 5928589, "path": "Publication:SCHUETZ-2024-SIMLOD", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/SCHUETZ-2024-SIMLOD/SCHUETZ-2024-SIMLOD-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/SCHUETZ-2024-SIMLOD/SCHUETZ-2024-SIMLOD-paper:thumb{{size}}.png" }, { "description": "Rendered Point Cloud to the left and points/voxels colored by the containing octree node to the right.", "filetitle": "SimLOD", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1911, "image_height": 1072, "name": "SCHUETZ-2024-SIMLOD-SimLOD.jpg", "type": "image/jpeg", "size": 323598, "path": "Publication:SCHUETZ-2024-SIMLOD", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/SCHUETZ-2024-SIMLOD/SCHUETZ-2024-SIMLOD-SimLOD.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/SCHUETZ-2024-SIMLOD/SCHUETZ-2024-SIMLOD-SimLOD:thumb{{size}}.png" } ], "projects_workgroups": [ "d9275", "d4386" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/SCHUETZ-2024-SIMLOD/", "__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" } ]