Software & Data Downloads — FRPC

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.

• Related Publications

   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}
  • }

Access software at https://github.com/merlresearch/FRPC.