Cylindrical coordinates for LiDAR point cloud compression

TL;DR

This paper introduces a cylindrical coordinate-based voxelization method for LiDAR point cloud compression, leveraging the sensor's circular scanning pattern to improve encoding efficiency and reduce bitrate.

Contribution

It extends RAHT for cylindrical coordinates and demonstrates superior compression performance over Cartesian-based methods for LiDAR data.

Findings

5-10% bitrate reduction in attribute coding

35-45% reduction in octree bits

Improved compression efficiency for LiDAR point clouds

Abstract

We present an efficient voxelization method to encode the geometry and attributes of 3D point clouds obtained from autonomous vehicles. Due to the circular scanning trajectory of sensors, the geometry of LiDAR point clouds is inherently different from that of point clouds captured from RGBD cameras. Our method exploits these specific properties to representing points in cylindrical coordinates instead of conventional Cartesian coordinates. We demonstrate thatRegion Adaptive Hierarchical Transform (RAHT) can be extended to this setting, leading to attribute encoding based on a volumetric partition in cylindrical coordinates. Experimental results show that our proposed voxelization outperforms conventional approaches based on Cartesian coordinates for this type of data. We observe a significant improvement in attribute coding performance with 5-10%reduction in bitrate and octree representation with 35-45% reduction in bits.