[ { "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": "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": "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": "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. ", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "fragcount", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1299, "image_height": 985, "name": "SCHUETZ-2021-PCC-fragcount.jpg", "type": "image/jpeg", "size": 836239, "path": "Publication:SCHUETZ-2021-PCC", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/SCHUETZ-2021-PCC/SCHUETZ-2021-PCC-fragcount.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/SCHUETZ-2021-PCC/SCHUETZ-2021-PCC-fragcount:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1116, 1650, 193 ], "booktitle": "techreport", "cfp": { "name": "call-for-papers.pdf", "type": "application/pdf", "error": "0", "size": "6107441", "orig_name": "call-for-papers.pdf", "ext": "pdf" }, "date_from": "2021-06-29", "date_to": "2021-07-02", "doi": "10.1111/cgf.14345", "event": "Eurographics Symposium on Rendering 2021 (EGSR '21)", "first_published": "2021-07", "issn": "1467-8659", "journal": "Computer Graphics Forum", "lecturer": [ 1116 ], "location": "online", "number": "4", "open_access": "yes", "pages": "12", "pages_from": "115", "pages_to": "126", "publisher": "Eurographics Association", "volume": "40", "research_areas": [ "Rendering" ], "keywords": [ "point-based rendering", "compute shader", "real-time rendering" ], "weblinks": [ { "href": "https://github.com/m-schuetz/compute_rasterizer", "caption": "Source Code", "description": null, "main_file": 0 } ], "files": [ { "description": "appendix", "filetitle": "appendix", "main_file": false, "use_in_gallery": false, "access": "public", "name": "SCHUETZ-2021-PCC-appendix.pdf", "type": "application/pdf", "size": 5715741, "path": "Publication:SCHUETZ-2021-PCC", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/SCHUETZ-2021-PCC/SCHUETZ-2021-PCC-appendix.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/SCHUETZ-2021-PCC/SCHUETZ-2021-PCC-appendix:thumb{{size}}.png" }, { "description": null, "filetitle": "fragcount", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1299, "image_height": 985, "name": "SCHUETZ-2021-PCC-fragcount.jpg", "type": "image/jpeg", "size": 836239, "path": "Publication:SCHUETZ-2021-PCC", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/SCHUETZ-2021-PCC/SCHUETZ-2021-PCC-fragcount.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/SCHUETZ-2021-PCC/SCHUETZ-2021-PCC-fragcount:thumb{{size}}.png" }, { "description": "paper", "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "SCHUETZ-2021-PCC-paper.pdf", "type": "application/pdf", "size": 4255515, "path": "Publication:SCHUETZ-2021-PCC", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/SCHUETZ-2021-PCC/SCHUETZ-2021-PCC-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/SCHUETZ-2021-PCC/SCHUETZ-2021-PCC-paper:thumb{{size}}.png" }, { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 2000, "image_height": 625, "name": "SCHUETZ-2021-PCC-teaser.jpg", "type": "image/jpeg", "size": 722844, "path": "Publication:SCHUETZ-2021-PCC", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/SCHUETZ-2021-PCC/SCHUETZ-2021-PCC-teaser.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/SCHUETZ-2021-PCC/SCHUETZ-2021-PCC-teaser:thumb{{size}}.png" } ], "projects_workgroups": [ "d4386", "3DSpatialization" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2021/SCHUETZ-2021-PCC/", "__class": "Publication" } ]