[ { "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", "description": "Datasets, models, more results", "main_file": 0 } ], "files": [ { "description": "Datasets for training and testing, containing chunk points and GT patches", "filetitle": "Datasets", "main_file": false, "use_in_gallery": false, "access": "public", "name": "erler-2025-lidarscout-Datasets.zip", "type": "application/x-zip-compressed", "size": 3201189460, "path": "Publication:erler-2025-lidarscout", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/erler-2025-lidarscout/erler-2025-lidarscout-Datasets.zip", "thumb_image_sizes": [] }, { "description": "Models with checkpoints, training logs, hyperparameters and TorchScript", "filetitle": "Models", "main_file": false, "use_in_gallery": false, "access": "public", "name": "erler-2025-lidarscout-Models.zip", "type": "application/x-zip-compressed", "size": 185148024, "path": "Publication:erler-2025-lidarscout", "url": 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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": "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": "herzberger-2024-roh", "type_id": "journalpaper", "tu_id": null, "repositum_id": "20.500.12708/193209", "title": "Residency Octree: a hybrid approach for scalable web-based multi-volume rendering", "date": "2024-01", "abstract": "We present a hybrid multi-volume rendering approach based on a novel Residency Octree that combines the advantages of out-of-core volume rendering using page tables with those of standard octrees. Octree approaches work by performing hierarchical tree traversal. However, in octree volume rendering, tree traversal and the selection of data resolution are intrinsically coupled. This makes fine-grained empty-space skipping costly. Page tables, on the other hand, allow access to any cached brick from any resolution. However, they do not offer a clear and efficient strategy for substituting missing high-resolution data with lower-resolution data. We enable flexible mixed-resolution out-of-core multi-volume rendering by decoupling the cache residency of multi-resolution data from a resolution-independent spatial subdivision determined by the tree. Instead of one-to-one node-to-brick correspondences, each residency octree node is mapped to a set of bricks from different resolution levels. This makes it possible to efficiently and adaptively choose and mix resolutions, adapt sampling rates, and compensate for cache misses. At the same time, residency octrees support fine-grained empty-space skipping, independent of the data subdivision used for caching. Finally, to facilitate collaboration and outreach, and to eliminate local data storage, our implementation is a web-based, pure client-side renderer using WebGPU and WebAssembly. Our method is faster than prior approaches and efficient for many data channels with a flexible and adaptive choice of data resolution.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2656, "image_height": 912, "name": "herzberger-2024-roh-teaser.png", "type": "image/png", "size": 550157, "path": "Publication:herzberger-2024-roh", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/herzberger-2024-roh/herzberger-2024-roh-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/herzberger-2024-roh/herzberger-2024-roh-teaser:thumb{{size}}.png" }, "sync_repositum_override": "projects,date_from,date_to,event,lecturer,location", "repositum_presentation_id": null, "authors": [ 1857, 226, 5321, 5322, 1933, 166, 818 ], "date_from": "2023-10-22", "date_to": "2023-10-27", "doi": "10.1109/TVCG.2023.3327193", "event": "IEEE VIS 2023", "issn": "1941-0506", "journal": "IEEE Transactions on Visualization and Computer Graphics", "lecturer": [ 1857 ], "location": "Melbourne, Victoria, Australia", "number": "1", "open_access": "yes", "pages": "11", "pages_from": "1380", "pages_to": "1390", "publisher": "IEEE", "volume": "30", "research_areas": [], "keywords": [ "Rendering (computer graphics)", "Octrees", "Spatial resolution", "Graphics processing units", "Data visualization", "Optimization", "Standards", "Volume rendering", "Ray-guided rendering", "Large-scale data", "Out-of-core rendering", "Multi-resolution", "Multi-channel", "Web-Based Visualization" ], "weblinks": [], "files": [ { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "herzberger-2024-roh-paper.pdf", "type": "application/pdf", "size": 8426051, "path": "Publication:herzberger-2024-roh", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/herzberger-2024-roh/herzberger-2024-roh-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/herzberger-2024-roh/herzberger-2024-roh-paper:thumb{{size}}.png" }, { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2656, "image_height": 912, "name": "herzberger-2024-roh-teaser.png", "type": "image/png", "size": 550157, "path": "Publication:herzberger-2024-roh", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/herzberger-2024-roh/herzberger-2024-roh-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/herzberger-2024-roh/herzberger-2024-roh-teaser:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/herzberger-2024-roh/", "__class": "Publication" }, { "id": "herzberger-2023-swv", "type_id": "masterthesis", "tu_id": null, "repositum_id": "20.500.12708/188243", "title": "Scalable Web-based Volume Rendering for Large-scale Biomedical Data Sets", "date": "2023-07", "abstract": "Recent advances in imaging modalities produce large-scale volumetric data sets with a large number of channels. Interactive visualization of such data sets requires out-of-core direct volume rendering (DVR) methods such as octrees or page-table hierarchies. For this reason, data sets are both down-sampled into a multi-resolution hierarchy and divided into smaller bricks, in order to stream only those parts of the volume contributing to the rendered image to the GPU. Furthermore, rendering multiple channels requires careful optimization because the high computational cost of DVR grows with the number of channels to render. A common optimization in DVR is empty-space skipping where fully translucent regions in the volume are not sampled to reduce the number of loop iterations and texture look-ups during rendering. Previous out-of-core DVR methods are designed for single-channel volumes and are only suitable for multi-channel volumes to a limited extent. In octree-based methods, accessing cached volume data requires traversing the tree for each sample and channel. Furthermore, in previous approaches, the spatial subdivision of the octree is intrinsically coupled to the down-sampling ratio and bricking granularity in the data set. This leads to suboptimal cache utilization and makes fine-grained empty-space skipping costly. Page-table hierarchies, on the other hand, allow access to any cached brick from any resolution without traversing a tree structure. However, their support for empty-space skipping is also tied to the bricking granularity in the data set and is thus limited. We present a hybrid multi-volume rendering approach based on a novel Residency Octree that combines the advantages of out-of-core volume rendering using page tables with those of standard octrees. We enable flexible mixed-resolution out-of-core multi- volume rendering by decoupling the cache residency of multi-resolution data from a resolution-independent spatial subdivision determined by the tree. Instead of one-to-one node-to-brick correspondences, each residency octree node is mapped to a set of bricks in each resolution level. This makes it possible to efficiently and adaptively choose and mix resolutions, adapt sampling rates, and compensate for cache misses. At the same time, residency octrees support fine-grained empty-space skipping, independent of the data subdivision used for caching. Finally, to facilitate collaboration and outreach, and to eliminate local data storage, our implementation is a web-based, pure client-side renderer using WebGPU and WebAssembly. Our method is faster than prior approaches and efficient for many data channels with a flexible and adaptive choice of data resolution.", "authors_et_al": false, "substitute": null, "main_image": { "description": "Figure 1.1: Overview of our method. Volume bricks of different resolution levels and channels are streamed into a\nbrick cache (a), and referenced via a multi-channel page-table hierarchy (b). The residency octree (c) keeps track of the\ncorrespondence between spatial regions and the cache residency of bricks of different resolutions, enabling mixed-resolution,\nmulti-channel rendering (d) with efficient, adaptive substitution of missing higher resolutions by available lower resolutions.\nTraversal happens for spatial regions corresponding to octree nodes instead of memory pages and is also independent of the\nnumber of channels. (e) 16-channel rendering of melanoma.", "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 3174, "image_height": 1167, "name": "herzberger-2023-swv-teaser.png", "type": "image/png", "size": 930066, "path": "Publication:herzberger-2023-swv", "url": "https://www.cg.tuwien.ac.at/research/publications/2023/herzberger-2023-swv/herzberger-2023-swv-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2023/herzberger-2023-swv/herzberger-2023-swv-teaser:thumb{{size}}.png" }, "sync_repositum_override": "date,projects,co_supervisor", "repositum_presentation_id": null, "authors": [ 1857 ], "co_supervisor": [ 818 ], "date_end": "2023-07", "date_start": "2022-10", "diploma_examina": "2023-09-27", "doi": "10.34726/hss.2023.106203", "matrikelnr": "01006039", "open_access": "yes", "pages": "99", "supervisor": [ 166 ], "research_areas": [], "keywords": [ "volume rendering", "ray-guided rendering", "large-scale data", "out-of-core rendering", "multi-resolution data", "multi-channel volumes", "web-based visualization" ], "weblinks": [], "files": [ { "description": "Figure 1.1: Overview of our method. Volume bricks of different resolution levels and channels are streamed into a\nbrick cache (a), and referenced via a multi-channel page-table hierarchy (b). The residency octree (c) keeps track of the\ncorrespondence between spatial regions and the cache residency of bricks of different resolutions, enabling mixed-resolution,\nmulti-channel rendering (d) with efficient, adaptive substitution of missing higher resolutions by available lower resolutions.\nTraversal happens for spatial regions corresponding to octree nodes instead of memory pages and is also independent of the\nnumber of channels. 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