SimLOD: Simultaneous LOD Generation and Rendering

Information

• Publication Type: Journal Paper with Conference Talk
• Workgroup(s)/Project(s):
  • Instant Visualization and Interaction for Large Point Clouds
• Date: May 2024
• Journal: Proceedings of the ACM in Computer Graphics and Interactive Techniques
• Volume: 7
• Note: Source Code: https://github.com/m-schuetz/SimLOD
• Lecturer: Markus Schütz
• Event: I3D
• Conference date: May 2023
• Pages: 20 – 20

Abstract

About: 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.

Background: 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. This approach allows users to view intermediate results right away.

Results: 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 from a PCIe 5.0 SSD) on an RTX 4090. 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.

Discussion/Limitations: We aim to provide near-instant, real-time visualization of large data sets without preprocessing. Out-of-core processing of arbitrarily large data sets and color-filtering for higher-quality LODs are subject to future work.

Additional Files and Images

Weblinks

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BibTeX

@article{SCHUETZ-2024-SIMLOD,
  title =      "SimLOD: Simultaneous LOD Generation and Rendering",
  author =     "Markus Sch\"{u}tz and Lukas Herzberger and Michael Wimmer",
  year =       "2024",
  abstract =   "About: 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.  Background: 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. This approach allows users to
               view intermediate results right away.  Results: 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 from a PCIe 5.0 SSD) on an RTX 4090. 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.  Discussion/Limitations:  We aim to provide
               near-instant, real-time visualization of large data sets
               without preprocessing. Out-of-core processing of arbitrarily
               large data sets and color-filtering for higher-quality LODs
               are subject to future work.",
  month =      may,
  journal =    "Proceedings of the ACM in Computer Graphics and Interactive
               Techniques",
  volume =     "7",
  note =       "Source Code: https://github.com/m-schuetz/SimLOD",
  pages =      "20--20",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2024/SCHUETZ-2024-SIMLOD/",
}