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Fast Resampling on Point Clouds via Graphs for demonstrating how to use a graph based filter to conduct subsampling on an input point cloud.

We propose a randomized resampling strategy to reduce the cost of storing, processing and visualizing a large-scale point cloud, 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 3D space.

  •  Chen, S., Tian, D., Feng, C., Vetro, A., Kovacevic, J., "Fast Resampling of 3D Point Clouds via Graphs", IEEE Transactions on Signal Processing, DOI: 10.1109/​TSP.2017.2771730, Vol. 66, No. 3, pp. 666-681, November 2017.
    BibTeX TR2017-215 PDF Software
    • @article{Chen2017nov,
    • author = {Chen, Siheng and Tian, Dong and Feng, Chen and Vetro, Anthony and Kovacevic, Jelena},
    • title = {Fast Resampling of 3D Point Clouds via Graphs},
    • journal = {IEEE Transactions on Signal Processing},
    • year = 2017,
    • volume = 66,
    • number = 3,
    • pages = {666--681},
    • month = nov,
    • doi = {10.1109/TSP.2017.2771730},
    • url = {}
    • }

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