Voxel-based Point Cloud Geometry Compression with Space-to-Channel Context

• URL: http://arxiv.org/abs/2503.18283v1
• Date: Mon, 24 Mar 2025 01:56:08 GMT
• Title: Voxel-based Point Cloud Geometry Compression with Space-to-Channel Context
• Authors: Bojun Liu, Yangzhi Ma, Ao Luo, Li Li, Dong Liu,
• Abstract summary: We introduce a stage-wise Space-to-Channel (S2C) context model for both dense point clouds and low-level sparse point clouds.<n>For high-level sparse point clouds, we propose a level-wise S2C context model that addresses resolution limitations.<n> Experimental results show that our S2C context model achieves bit savings while maintaining or improving reconstruction quality.
• Score: 12.978693110398432
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Voxel-based methods are among the most efficient for point cloud geometry compression, particularly with dense point clouds. However, they face limitations due to a restricted receptive field, especially when handling high-bit depth point clouds. To overcome this issue, we introduce a stage-wise Space-to-Channel (S2C) context model for both dense point clouds and low-level sparse point clouds. This model utilizes a channel-wise autoregressive strategy to effectively integrate neighborhood information at a coarse resolution. For high-level sparse point clouds, we further propose a level-wise S2C context model that addresses resolution limitations by incorporating Geometry Residual Coding (GRC) for consistent-resolution cross-level prediction. Additionally, we use the spherical coordinate system for its compact representation and enhance our GRC approach with a Residual Probability Approximation (RPA) module, which features a large kernel size. Experimental results show that our S2C context model not only achieves bit savings while maintaining or improving reconstruction quality but also reduces computational complexity compared to state-of-the-art voxel-based compression methods.

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