OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression

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.

1. Introduction

In the past few decades, we have witnessed artificial intelligence revolutionizing robotic perception. Robots powered by these AI algorithms often utilize a plethora of different sensors to perceive and interact with the world. In particular, 3D sensors such as LiDAR and structured light cameras have proven to be crucial for many types of robots, such as self-driving cars, indoor rovers, robot arms, and drones, thanks to their ability to accurately capture the 3D geometry of a scene. These sensors produce a significant amount of data: a single Velodyne HDL-64 LiDAR sensor generates over 100,000 points per sweep, resulting in over 84 billion points per day. This enormous quantity of raw sensor data brings challenges to onboard and offboard storage as well as real-time communication. Hence, it is necessary to develop an efficient compression method for 3D point clouds.