Segmentation

Artificial intelligence

Remote sensing

Physics

Redundancy (engineering)

Lidar

Data compression

Geology

Point cloud

Memory footprint

Quantum mechanics

Operating system

Computer science

Entropy (arrow of time)

Algorithm

Octree

Computer vision

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 point cloud into an octree, a data-efficient structure suitable for sparse point clouds. We then design a tree-structured conditional entropy model that can be directly applied to octree structures to predict the probability of a symbol's occurrence. 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. This helps advance the feasibility of using point cloud compression to reduce the onboard and offboard storage for safety-critical applications such as self-driving cars, where a single vehicle captures 84 billion points per day.

OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression