Abstract
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 language | English (US) |
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Pages (from-to) | 666-681 |
Number of pages | 16 |
Journal | IEEE Transactions on Signal Processing |
Volume | 66 |
Issue number | 3 |
DOIs | |
State | Published - Feb 1 2018 |
Keywords
- 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