Fast Run-Length Compression of Point Cloud Geometry

Dion E.O. Tzamarias*, Kevin Chow, Ian Blanes, Joan Serra-Sagrista

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

6 Citations (Scopus)
3 Downloads (Pure)


The increase in popularity of point-cloud-oriented applications has triggered the development of specialized compression algorithms. In this paper, a novel algorithm is developed for the lossless geometry compression of voxelized point clouds following an intra-frame design. The encoded voxels are arranged into runs and are encoded through a single-pass application directly on the voxel domain. This is done without representing the point cloud via an octree nor rendering the voxel space through an occupancy matrix, therefore decreasing the memory requirements of the method. Each run is compressed using a context-adaptive arithmetic encoder yielding state-of-the-art compression results, with gains of up to 15% over TMC13, MPEG's standard for point cloud geometry compression. Several proposed contributions accelerate the calculations of each run's probability limits prior to arithmetic encoding. As a result, the encoder attains a low computational complexity described by a linear relation to the number of occupied voxels leading to an average speedup of 1.8 over TMC13 in encoding speeds. Various experiments are conducted assessing the proposed algorithm's state-of-the-art performance in terms of compression ratio and encoding speeds.

Original languageEnglish
Pages (from-to)4490-4501
Number of pages12
JournalIEEE transactions on image processing
Publication statusPublished - 2022


  • context-adaptive encoder
  • Point cloud geometry compression
  • run-length encoding


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