Voxel-based Point Cloud Geometry Compression with Space-to-Channel Context
This work addresses compression efficiency for point clouds, particularly dense and sparse types, with incremental improvements over existing voxel-based approaches.
The paper tackles the limited receptive field in voxel-based point cloud geometry compression by introducing a Space-to-Channel (S2C) context model, achieving bit savings and reduced computational complexity compared to state-of-the-art methods.
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.