TR2017-215

Fast Resampling of 3D Point Clouds via Graphs


    •  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 = {https://www.merl.com/publications/TR2017-215}
      • }
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  • Research Area:

    Digital Video

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 3D space. We then specify the featureextraction operator to be a graph filter and study specific resampling strategies based on allpass, lowpass, highpass 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.

 

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