MEGA-PCC: Efficient Point Cloud Compression with Mamba
Analysis
This paper introduces MEGA-PCC, a novel end-to-end learning-based framework for joint point cloud geometry and attribute compression. It addresses limitations of existing methods by eliminating post-hoc recoloring and manual bitrate tuning, leading to a simplified and optimized pipeline. The use of the Mamba architecture for both the main compression model and the entropy model is a key innovation, enabling effective modeling of long-range dependencies. The paper claims superior rate-distortion performance and runtime efficiency compared to existing methods, making it a significant contribution to the field of 3D data compression.
Key Takeaways
- •Proposes MEGA-PCC, an end-to-end learning-based framework for joint point cloud compression.
- •Employs Mamba architecture for both the main compression model and the entropy model.
- •Eliminates post-hoc recoloring and manual bitrate tuning.
- •Achieves superior rate-distortion performance and runtime efficiency compared to baselines.
“MEGA-PCC achieves superior rate-distortion performance and runtime efficiency compared to both traditional and learning-based baselines.”