SCP: Spherical-Coordinate-based Learned Point Cloud Compression

TL;DR

This paper introduces SCP, a spherical-coordinate-based learned point cloud compression method that leverages azimuthal invariance features of spinning LiDAR data, improving compression efficiency significantly.

Contribution

The paper proposes a novel spherical-coordinate-based approach with a multi-level Octree to enhance learned point cloud compression, addressing azimuthal invariance and distant area reconstruction.

Findings

SCP surpasses previous methods by up to 29.14% in PSNR BD-Rate.

The approach effectively leverages azimuthal invariance features.

SCP demonstrates high universality across different compression techniques.

Abstract

In recent years, the task of learned point cloud compression has gained prominence. An important type of point cloud, the spinning LiDAR point cloud, is generated by spinning LiDAR on vehicles. This process results in numerous circular shapes and azimuthal angle invariance features within the point clouds. However, these two features have been largely overlooked by previous methodologies. In this paper, we introduce a model-agnostic method called Spherical-Coordinate-based learned Point cloud compression (SCP), designed to leverage the aforementioned features fully. Additionally, we propose a multi-level Octree for SCP to mitigate the reconstruction error for distant areas within the Spherical-coordinate-based Octree. SCP exhibits excellent universality, making it applicable to various learned point cloud compression techniques. Experimental results demonstrate that SCP surpasses previous state-of-the-art methods by up to 29.14% in point-to-point PSNR BD-Rate.