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

本论文提出了一种新颖的深度压缩算法，通过将LiDAR点云编码为八叉树，并设计一种以树结构为基础的条件熵模型，来减少点云数据的储存空间。实验证明该方法在保证重建质量的同时，将比特率降低了10-20%，并在下游3D分割和检测任务中表现突出，非常适用于自驾车等实时应用场景。

OctSqueeze: 基于八叉树结构的激光雷达熵编码模型