A Novel Point Cloud Compression Algorithm Based on Clustering

Xuebin Sun, Han Ma, Yuxiang Sun, Ming Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

63 Citations (Scopus)


Due to the enormous volume of point cloud data, transmitting and storing the data requires large bandwidth and storage space. It could be a critical bottleneck, especially in tasks such as autonomous driving. In this letter, we propose a novel point cloud compression algorithm based on clustering. The proposed scheme starts with a range image-based segmentation step, which segments the three-dimensional (3-D) range data into ground and main objects. Then, it introduces a novel prediction method according to the segmented regions' shape. This prediction method is inspired by the depth modeling modes used in 3-D high-efficiency video coding for depth map coding. Finally, the few prediction residual is efficiently compressed with several lossless or lossy data compression techniques. Experimental results show that the proposed algorithm can largely eliminate the spatial redundant information of the point cloud data. The lossless compression scheme reaches a compression ratio of nearly 5%, which means that the point cloud is compressed to 5% of its original size without any distance distortion. Compared with other methods, the proposed compression algorithm also shows better performance.

Original languageEnglish
Article number8648155
Pages (from-to)2132-2139
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number2
Publication statusPublished - Apr 2019


  • automation technologies for smart cities
  • Range Sensing
  • SLAM

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


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