Dependence-Based Coarse-to-Fine Approach for Reducing Distortion Accumulation in G-PCC Attribute Compression

Geometry-based point cloud compression (G-PCC) is a state-of-the-art point cloud compression standard. While G-PCC achieves excellent performance, its reliance on the predicting transform leads to a significant dependence problem, which can easily result in distortion accumulation. This not only increases bitrate consumption but also degrades reconstruction quality. To address these challenges, we propose a dependence-based coarse-to-fine approach for distortion accumulation in G-PCC attribute compression. Our method consists of three modules: level-based adaptive quantization, point-based adaptive quantization, and Wiener filter-based refinement level quality enhancement. The level-based adaptive quantization module addresses the interlevel-of-detail (LOD) dependence problem, while the point-based adaptive quantization module tackles the interpoint dependence problem. On the other hand, the Wiener filter-based refinement level quality enhancement module enhances the reconstruction quality of each point based on the dependence order among LODs. Extensive experimental results demonstrate the effectiveness of the proposed method. Notably, when the proposed method was implemented in the latest G-PCC test model (TMC13v23.0), a Bj$\phi$ntegaard delta rate of $-$4.9%, $-$12.7%, and $-$14.0% was achieved for the Luma, Chroma Cb, and Chroma Cr components, respectively.

I. Introduction

The rapid advancement of 3-D sensing and capturing technology has led to a growing interest in 3-D point clouds (3-D PCs) [1]. 3-D PCs play a crucial role in various fields, such as virtual reality, autonomous driving [2], [3], immersive communication, and 3-D modeling. 3-D PCs represent objects and scenes as a collection of points with geometry coordinates and attribute information, such as color, reflectance, and normal vectors [4], [5], [6]. Recent advances in rendering and sensing technologies have enabled the creation of highly detailed 3-D PCs that consist of millions or even billions of points. However, the storage and transmission of such large datasets present significant challenges. For instance, to store a static 3-D PC consisting of one million points, about million bits are required ( bits per point for the 3-D coordinates and bits per point for the color information). For a dynamic 3-D PC with a frame rate of 30 frames per second, the data rate can reach million bits per second, exceeding the bandwidth capacity of current networks. Therefore, efficient point cloud compression is essential to tackle these challenges.