MEGA-PCC: A Mamba-based Efficient Approach for Joint Geometry and Attribute Point Cloud Compression

TL;DR

MEGA-PCC introduces an end-to-end learning-based framework for joint point cloud geometry and attribute compression, improving efficiency and performance by eliminating heuristic tuning and leveraging a Mamba-based entropy model.

Contribution

It presents a novel fully end-to-end joint compression method with a shared encoder and dual decoders, utilizing Mamba architecture for better dependency modeling and entropy coding.

Findings

Achieves superior rate-distortion performance.

Reduces system complexity by removing recoloring and heuristic tuning.

Improves runtime efficiency over traditional methods.

Abstract

Joint compression of point cloud geometry and attributes is essential for efficient 3D data representation. Existing methods often rely on post-hoc recoloring procedures and manually tuned bitrate allocation between geometry and attribute bitstreams in inference, which hinders end-to-end optimization and increases system complexity. To overcome these limitations, we propose MEGA-PCC, a fully end-to-end, learning-based framework featuring two specialized models for joint compression. The main compression model employs a shared encoder that encodes both geometry and attribute information into a unified latent representation, followed by dual decoders that sequentially reconstruct geometry and then attributes. Complementing this, the Mamba-based Entropy Model (MEM) enhances entropy coding by capturing spatial and channel-wise correlations to improve probability estimation. Both models are built on the Mamba architecture to effectively model long-range dependencies and rich contextual features. By eliminating the need for recoloring and heuristic bitrate tuning, MEGA-PCC enables data-driven bitrate allocation during training and simplifies the overall pipeline. Extensive experiments demonstrate that MEGA-PCC achieves superior rate-distortion performance and runtime efficiency compared to both traditional and learning-based baselines, offering a powerful solution for AI-driven point cloud compression.