Temporal Octrees for Compressing Dynamic Point Cloud Streams

Marcos Slomp, Hiroshi Kawasaki, Ryo Furukawa, Ryusuke Sagawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Range-based scanners built upon multiple cameras and projectors offer affordable, entire-shape and high-speed setups for 3D scanning. The point cloud streams produced by these devices require large amounts of storage space. Compressing these datasets is challenging since the capturing process may result in noise and surface irregularities, and consecutive frames can differ substantially in the overall point distribution. Exploiting spatial and temporal coherency is difficult on such conditions, but nonetheless crucial for achieving decent compression rates. This paper introduces a novel data structure, the temporal sparse voxel octree, capable of grouping spatio-temporal coherency of multiple point cloud streams into a single voxel hierarchy. In the data structure, a bit mask is attached to each node, existing nodes can then be reused at different frames by manipulating their bit masks, providing substantial memory savings. Although the technique yields some losses, the amount of loss can be controlled.

Original languageEnglish
Title of host publicationProceedings - 2014 International Conference on 3D Vision Workshops, 3DV 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages49-56
Number of pages8
ISBN (Electronic)9781479970018
DOIs
Publication statusPublished - Aug 7 2015
Externally publishedYes
Event2nd International Conference on 3D Vision Workshops, 3DV 2014 - Tokyo, Japan
Duration: Dec 8 2014Dec 11 2014

Other

Other2nd International Conference on 3D Vision Workshops, 3DV 2014
CountryJapan
CityTokyo
Period12/8/1412/11/14

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Slomp, M., Kawasaki, H., Furukawa, R., & Sagawa, R. (2015). Temporal Octrees for Compressing Dynamic Point Cloud Streams. In Proceedings - 2014 International Conference on 3D Vision Workshops, 3DV 2014 (pp. 49-56). [7182716] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/3DV.2014.79