TR2017-017

Contour-Enhanced Resampling of 3D Point Clouds Via Graphs


    •  Chen, S., Tian, D., Feng, C., Vetro, A., Kovacevic, J., "Contour-Enhanced Resampling of 3D Point Clouds Via Graphs", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2017.
      BibTeX TR2017-017 PDF
      • @inproceedings{Chen2017mar,
      • author = {Chen, Siheng and Tian, Dong and Feng, Chen and Vetro, Anthony and Kovacevic, Jelena},
      • title = {Contour-Enhanced Resampling of 3D Point Clouds Via Graphs},
      • booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
      • year = 2017,
      • month = mar,
      • url = {https://www.merl.com/publications/TR2017-017}
      • }
  • MERL Contacts:
  • Research Area:

    Digital Video

To reduce storage and computational cost for processing and visualizing large-scale 3D point clouds, an efficient resampling strategy is needed to select a representative subset of 3D points that can preserve contours in the original 3D point cloud. We tackle this problem by using graph-based techniques as graphs can represent underlying surfaces and lend themselves well to efficient computation. We first construct a general graph for a 3D point cloud and then propose a graphbased metric to quantify the contour information via highpass graph filtering. Finally, we obtain an optimal resampling distribution that preserves the contour information by solving an optimization problem. When browsing, the proposed graph-based resampling performs better than uniform resampling both for toy point clouds as well as real large-scale point clouds. Furthermore, as neither mesh construction nor surface normal calculation is involved, the proposed graph-based method is computationally more efficient than the mesh-based methods.

 

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    •  NEWS   MERL to present 10 papers at ICASSP 2017
      Date: March 5, 2017 - March 9, 2017
      Where: New Orleans
      MERL Contacts: Petros Boufounos; Takaaki Hori; Jonathan Le Roux; Dehong Liu; Hassan Mansour; Anthony Vetro; Ye Wang
      Research Areas: Computer Vision, Computational Sensing, Digital Video, Information Security, Speech & Audio
      Brief
      • MERL researchers will presented 10 papers at the upcoming IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP), to be held in New Orleans from March 5-9, 2017. Topics to be presented include recent advances in speech recognition and audio processing; graph signal processing; computational imaging; and privacy-preserving data analysis.

        ICASSP is the flagship conference of the IEEE Signal Processing Society, and the world's largest and most comprehensive technical conference focused on the research advances and latest technological development in signal and information processing. The event attracts more than 2000 participants each year.
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