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

• URL: http://arxiv.org/abs/2512.22463v1
• Date: Sat, 27 Dec 2025 04:43:36 GMT
• Title: MEGA-PCC: A Mamba-based Efficient Approach for Joint Geometry and Attribute Point Cloud Compression
• Authors: Kai-Hsiang Hsieh, Monyneath Yim, Wen-Hsiao Peng, Jui-Chiu Chiang,
• Abstract summary: MEGA-PCC is a fully end-to-end, learning-based framework featuring two specialized models for joint compression.<n>It achieves superior rate-distortion performance and runtime efficiency compared to both traditional and learning-based baselines.
• Score: 9.422873276112067
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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