SOTAVerified

OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression

2020-05-14CVPR 2020Code Available0· sign in to hype

Lila Huang, Shenlong Wang, Kelvin Wong, Jerry Liu, Raquel Urtasun

arXivPDF

Code Available — Be the first to reproduce this paper.

Reproduce

Code

Abstract

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 effectiveness of our method over two large-scale datasets. The results demonstrate that our approach reduces the bitrate by 10-20% at the same reconstruction quality, compared to the previous state-of-the-art. Importantly, we also show that for the same bitrate, our approach outperforms other compression algorithms when performing downstream 3D segmentation and detection tasks using compressed representations. Our algorithm can be used to reduce the onboard and offboard storage of LiDAR points for applications such as self-driving cars, where a single vehicle captures 84 billion points per day

Tasks

modelSelf-Driving Cars

Reproductions