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Proceedings Paper

Temporally consistent segmentation of point clouds
Author(s): Jason L. Owens; Philip R. Osteen; Kostas Daniilidis
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Paper Abstract

We consider the problem of generating temporally consistent point cloud segmentations from streaming RGB-D data, where every incoming frame extends existing labels to new points or contributes new labels while maintaining the labels for pre-existing segments. Our approach generates an over-segmentation based on voxel cloud connectivity, where a modified k-means algorithm selects supervoxel seeds and associates similar neighboring voxels to form segments. Given the data stream from a potentially mobile sensor, we solve for the camera transformation between consecutive frames using a joint optimization over point correspondences and image appearance. The aligned point cloud may then be integrated into a consistent model coordinate frame. Previously labeled points are used to mask incoming points from the new frame, while new and previous boundary points extend the existing segmentation. We evaluate the algorithm on newly-generated RGB-D datasets.

Paper Details

Date Published: 3 June 2014
PDF: 16 pages
Proc. SPIE 9084, Unmanned Systems Technology XVI, 90840H (3 June 2014); doi: 10.1117/12.2050666
Show Author Affiliations
Jason L. Owens, U.S. Army Research Lab. (United States)
Philip R. Osteen, Engility Corp. (United States)
Kostas Daniilidis, Univ. of Pennsylvania (United States)

Published in SPIE Proceedings Vol. 9084:
Unmanned Systems Technology XVI
Robert E. Karlsen; Douglas W. Gage; Charles M. Shoemaker; Grant R. Gerhart, Editor(s)

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