Dynamic Downsampling Algorithm for 3D Point Cloud Map Based on Voxel Filtering

Wenqi Lyu, Wei Ke, Hao Sheng, Xiao Ma, Huayun Zhang

Research output: Contribution to journalArticlepeer-review


In response to the challenge of handling large-scale 3D point cloud data, downsampling is a common approach, yet it often leads to the problem of feature loss. We present a dynamic downsampling algorithm for 3D point cloud maps based on an improved voxel filtering approach. The algorithm consists of two modules, namely, dynamic downsampling and point cloud edge extraction. The former adapts voxel downsampling according to the features of the point cloud, while the latter preserves edge information within the 3D point cloud map. Comparative experiments with voxel downsampling, grid downsampling, clustering-based downsampling, random downsampling, uniform downsampling, and farthest-point downsampling were conducted. The proposed algorithm exhibited favorable downsampling simplification results, with a processing time of 0.01289 s and a simplification rate of 91.89%. Additionally, it demonstrated faster downsampling speed and showcased improved overall performance. This enhancement not only benefits productivity but also highlights the system’s efficiency and effectiveness.

Original languageEnglish
Article number3160
JournalApplied Sciences (Switzerland)
Issue number8
Publication statusPublished - Apr 2024


  • 3D point cloud downsampling
  • dynamic downsampling
  • improved voxel filtering
  • voxel filtering

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