Fast Out-of-Core Octree Generation for Massive Point Clouds

Information

  • Publication Type: Technical Report
  • Workgroup(s)/Project(s):
  • Date: August 2020
  • Number: TR-193-02-2020-3
  • Open Access: yes
  • Keywords: point clouds, point-based rendering, level of detail

Abstract

We propose an efficient out-of-core octree generation method for arbitrarily large point clouds. It utilizes a hierarchical counting sort to quickly split the point cloud into small chunks, which are then processed in parallel. Levels of detail are generated by subsampling the full data set bottom up using one of multiple exchangeable sampling strategies. We introduce a fast hierarchical approximate blue-noise strategy and compare it to a uniform random sampling strategy. The throughput, including out-of-core access to disk, generating the octree, and writing the final result to disk, is about an order of magnitude faster than the state of the art, and reaches up to around 6 million points per second for the blue-noise approach and up to around 9 million points per second for the uniform random approach on modern SSDs.

Additional Files and Images

Additional images and videos


Additional files

Weblinks

  • PotreeConverter 2.0 at github
    PotreeConverter generates an octree LOD structure for streaming and real-time rendering of massive point clouds. The results can be viewed in web browsers with Potree or as a desktop application with PotreeDesktop.

  • Video

BibTeX

@techreport{SCHUETZ-2020-MPC,
  title =      "Fast Out-of-Core Octree Generation for Massive Point Clouds",
  author =     "Markus Sch\"{u}tz and Stefan Ohrhallinger and Michael Wimmer",
  year =       "2020",
  abstract =   "We propose an efficient out-of-core octree generation method
               for arbitrarily large point clouds. It utilizes a
               hierarchical counting sort to quickly split the point cloud
               into small chunks, which are then processed in parallel.
               Levels of detail are generated by subsampling the full data
               set bottom up using one of multiple exchangeable sampling
               strategies. We introduce a fast hierarchical approximate
               blue-noise strategy and compare it to a uniform random
               sampling strategy. The throughput, including out-of-core
               access to disk, generating the octree, and writing the final
               result to disk, is about an order of magnitude faster than
               the state of the art, and reaches up to around 6 million
               points per second for the blue-noise approach and up to
               around 9 million points per second for the uniform random
               approach on modern SSDs.",
  month =      aug,
  number =     "TR-193-02-2020-3",
  address =    "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
  institution = "Research Unit of Computer Graphics, Institute of Visual
               Computing and Human-Centered Technology, Faculty of
               Informatics, TU Wien ",
  note =       "human contact: technical-report@cg.tuwien.ac.at",
  keywords =   "point clouds, point-based rendering, level of detail",
  URL =        "/research/publications/2020/SCHUETZ-2020-MPC/",
}