TSC-PCAC: Voxel Transformer and Sparse Convolution Based Point Cloud Attribute Compression for 3D Broadcasting

TL;DR

This paper introduces TSC-PCAC, an innovative voxel transformer and sparse convolution-based method for efficient point cloud attribute compression, significantly reducing bitrate and encoding/decoding times for 3D broadcasting applications.

Contribution

The paper presents a novel end-to-end framework combining Transformer and sparse convolution modules with a two-stage local-global feature modeling approach for improved point cloud attribute compression.

Findings

Achieves over 38% bitrate reduction compared to existing methods.

Reduces encoding/decoding time by up to 98%.

Effectively captures local and global features for better compression.

Abstract

Point cloud has been the mainstream representation for advanced 3D applications, such as virtual reality and augmented reality. However, the massive data amounts of point clouds is one of the most challenging issues for transmission and storage. In this paper, we propose an end-to-end voxel Transformer and Sparse Convolution based Point Cloud Attribute Compression (TSC-PCAC) for 3D broadcasting. Firstly, we present a framework of the TSC-PCAC, which include Transformer and Sparse Convolutional Module (TSCM) based variational autoencoder and channel context module. Secondly, we propose a two-stage TSCM, where the first stage focuses on modeling local dependencies and feature representations of the point clouds, and the second stage captures global features through spatial and channel pooling encompassing larger receptive fields. This module effectively extracts global and local interpoint relevance to reduce informational redundancy. Thirdly, we design a TSCM based channel context module to exploit interchannel correlations, which improves the predicted probability distribution of quantized latent representations and thus reduces the bitrate. Experimental results indicate that the proposed TSC-PCAC method achieves an average of 38.53%, 21.30%, and 11.19% Bjontegaard Delta bitrate reductions compared to the Sparse-PCAC, NF-PCAC, and G-PCC v23 methods, respectively. The encoding/decoding time costs are reduced up to 97.68%/98.78% on average compared to the Sparse-PCAC. The source code and the trained models of the TSC-PCAC are available at https://github.com/igizuxo/TSC-PCAC.