Kavli Affiliate: Wei Gao
| First 5 Authors: Kangli Wang, Wei Gao, , ,
| Summary:
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.
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