Frame-Level Rate Control for Geometry-Based LiDAR Point Cloud Compression

The state-of-the-art compression method for Light Detection And Ranging (LiDAR) point clouds is the geometry-based point cloud compression (G-PCC) standard developed by Moving Pictures Experts Group immersive media working group (MPEG-I). However, there are currently no rate control algorithms designed specifically for Geometry-based LiDAR point cloud compression (G-LPCC). In this paper, we propose the first frame-level rate control algorithm for G-LPCC. We mainly have the following contributions in our proposed rate control algorithm. First, we model the rate-distortion (R-D) relationship for both the geometry and attribute. As the geometry bitrate is mainly determined by the frame-level geometry quantizer $Q_{G}$, we propose a relationship between the geometry bitrate and $Q_{G}$. In addition, as the attribute bitrate can be influenced by both the attribute quantizer $Q_{A}$ and $Q_{G}$, we build a relationship among the attribute bitrate, $Q_{G}$, and $Q_{A}$. Second, we propose a bit allocation algorithm between the geometry and attribute based on the R-D modeling. The $Q_{G}$ and $Q_{A}$ are modeled into a proper relationship to obtain geometry and attribute bits to achieve good R-D performance. Third, we propose using the point density of LiDAR point clouds to estimate the geometry model parameters. The point density is calculated using the average distance between each point and its nearest neighbor after excluding some noisy points. The proposed rate control algorithm is implemented in the G-PCC reference software. The experimental results show that the proposed rate control algorithm can control the bitrate accurately with satisfactory R-D performance.

I. Introduction

A point cloud is a set of points in 3D space that can be used to represent a 3D object or a 3D scene. Each point has a geometry position and a vector of attributes such as colors, normals, or material reflectance. An emerging use case of point clouds is in representing the environment surrounding a driving car. This kind of point clouds is usually captured by the Light Detection And Ranging (LiDAR) equipment [1] that is called as LiDAR point clouds in the following. LiDAR point clouds are potential to be widely used in auto-driving applications [2]. However, the high data rate is one of the key factors preventing the adoption of this media format. For example, for a LiDAR point cloud “Ford_01” [3], it has approximately 120 million points for 1500 frames. With the geometry and reflectance of each point represented by and 16 bits, respectively, the total size to represent the point cloud is as high as bits without compression. Recently, the Moving Pictures Experts Group (MPEG) immersive media working group (MPEG-I) develops a geometry-based point cloud compression (G-PCC) standard [4] to solve this problem.