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OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
We present a novel deep compression algorithm to reduce the memory footprint of LiDAR point clouds. Our method exploits the sparsity and structural redundancy between points to reduce the bitrate. Towards this goal, we first encode the LiDAR points into an octree, a data-efficient structure suitable for sparse point clouds. We then design a tree-structured conditional entropy model that models the probabilities of the octree symbols to encode the octree into a compact bitstream. We validate the
doi:10.1109/cvpr42600.2020.00139
dblp:conf/cvpr/HuangWWLU20
fatcat:2y2vyl2l2vepnarahdyzzhbovu