Real-Time Decompression and Rasterization of Massive Point Clouds

Information

• Publication Type: Journal Paper with Conference Talk
• Workgroup(s)/Project(s):
  • Rendering and Modeling
  • Instant Visualization and Interaction for Large Point Clouds
• Date: August 2024
• Journal: Proceedings of the ACM on Computer Graphics and Interactive Techniques
• Volume: 7
• Number: 3
• Location: Denver, USA
• Lecturer: Markus Schütz
• Article Number: 48
• ISSN: 2577-6193
• Event: High Performance Graphics
• DOI: 10.1145/3675373
• Pages: 15
• Publisher: Association for Computing Machinery (ACM)
• Conference date: 26. June 2024 – 28. June 2024
• Pages: 1 – 15
• Keywords: compression, point cloud, rasterization, real-time rendering

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.

Additional Files and Images

Weblinks

• Entry in reposiTUm (TU Wien Publication Database)
• DOI: 10.1145/3675373

BibTeX

@article{goel-2024-rdr,
  title =      "Real-Time Decompression and Rasterization of Massive Point
               Clouds",
  author =     "Rahul Goel and Markus Sch\"{u}tz and P. J. Narayanan and
               Bernhard Kerbl",
  year =       "2024",
  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.",
  month =      aug,
  journal =    "Proceedings of the ACM on Computer Graphics and Interactive
               Techniques",
  volume =     "7",
  number =     "3",
  articleno =  "48",
  issn =       "2577-6193",
  doi =        "10.1145/3675373",
  pages =      "15",
  publisher =  "Association for Computing Machinery (ACM)",
  pages =      "1--15",
  keywords =   "compression, point cloud, rasterization, real-time rendering",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2024/goel-2024-rdr/",
}