Temporally consistent segmentation of point clouds

Jason L. Owens; Philip R. Osteen; Kostas Daniilidis

doi:10.1117/12.2050666

3 June 2014 Temporally consistent segmentation of point clouds

Jason L. Owens, Philip R. Osteen, Kostas Daniilidis

Proceedings Volume 9084, Unmanned Systems Technology XVI; 90840H (2014) https://doi.org/10.1117/12.2050666
Event: SPIE Defense + Security, 2014, Baltimore, MD, United States

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.

Citation Download Citation

Jason L. Owens, Philip R. Osteen, and Kostas Daniilidis "Temporally consistent segmentation of point clouds", Proc. SPIE 9084, Unmanned Systems Technology XVI, 90840H (3 June 2014); https://doi.org/10.1117/12.2050666

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