UniPCGC: Towards Practical Point Cloud Geometry Compression via an Efficient Unified Approach
This work addresses limitations in learning-based point cloud compression for applications requiring efficient and flexible compression, though it is incremental in improving existing methods.
The paper tackles the problem of practical point cloud geometry compression by proposing UniPCGC, an efficient unified framework that supports lossy and lossless compression, variable rate, and variable complexity, achieving a compression ratio gain of 8.1% in lossless mode and a BD-Rate gain of 14.02% in lossy mode compared to previous state-of-the-art methods.
Learning-based point cloud compression methods have made significant progress in terms of performance. However, these methods still encounter challenges including high complexity, limited compression modes, and a lack of support for variable rate, which restrict the practical application of these methods. In order to promote the development of practical point cloud compression, we propose an efficient unified point cloud geometry compression framework, dubbed as UniPCGC. It is a lightweight framework that supports lossy compression, lossless compression, variable rate and variable complexity. First, we introduce the Uneven 8-Stage Lossless Coder (UELC) in the lossless mode, which allocates more computational complexity to groups with higher coding difficulty, and merges groups with lower coding difficulty. Second, Variable Rate and Complexity Module (VRCM) is achieved in the lossy mode through joint adoption of a rate modulation module and dynamic sparse convolution. Finally, through the dynamic combination of UELC and VRCM, we achieve lossy compression, lossless compression, variable rate and complexity within a unified framework. Compared to the previous state-of-the-art method, our method achieves a compression ratio (CR) gain of 8.1\% on lossless compression, and a Bjontegaard Delta Rate (BD-Rate) gain of 14.02\% on lossy compression, while also supporting variable rate and variable complexity.