Fast resampling of three-dimensional point clouds via graphs

Siheng Chen, Dong Tian, Chen Feng, Anthony Vetro, Jelena Kovačević

Research output: Contribution to journalArticlepeer-review


To reduce the cost of storing, processing, and visualizing a large-scale point cloud, we propose a randomized resampling strategy that selects a representative subset of points while preserving application-dependent features. The strategy is based on graphs, which can represent underlying surfaces and lend themselves well to efficient computation. We use a general feature-extraction operator to represent application-dependent features and propose a general reconstruction error to evaluate the quality of resampling; by minimizing the error, we obtain a general form of optimal resampling distribution. The proposed resampling distribution is guaranteed to be shift-, rotation- and scale-invariant in the three-dimensional space. We then specify the feature-extraction operator to be a graph filter and study specific resampling strategies based on all-pass, low-pass, high-pass graph filtering and graph filter banks. We validate the proposed methods on three applications: Large-scale visualization, accurate registration, and robust shape modeling demonstrating the effectiveness and efficiency of the proposed resampling methods.

Original languageEnglish (US)
Pages (from-to)666-681
Number of pages16
JournalIEEE Transactions on Signal Processing
Issue number3
StatePublished - Feb 1 2018


  • 3D point clouds
  • Contour detection
  • Graph filtering
  • Graph signal processing
  • Registration
  • Sampling
  • Shape modeling
  • Visualization

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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