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arXiv · 1702.06397

Fast Resampling of 3D Point Clouds via Graphs

Siheng Chen·Dong Tian·Chen Feng·Anthony Vetro·Jelena Kovačević
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Abstract

To reduce cost in storing, processing and visualizing a large-scale point cloud, we consider a randomized resampling strategy to select a representative subset of points while preserving application-dependent features. The proposed 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. We obtain a general form of optimal resampling distribution by minimizing the reconstruction error. The proposed optimal resampling distribution is guaranteed to be shift, rotation and scale-invariant in the 3D space. We next 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 finally apply the proposed methods to three applications: large-scale visualization, accurate registration and robust shape modeling. The empirical performance validates the effectiveness and efficiency of the proposed resampling methods.

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Siheng Chen, Dong Tian, Chen Feng, Anthony Vetro, Jelena Kovačević. 2017-02-11. Fast Resampling of 3D Point Clouds via Graphs. https://doi.org/10.1109/tsp.2017.2771730

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