Point Cloud Attribute Compression With Geometry-Aware Lifting-Based Multiscale Networks

Point cloud attribute compression is challenged by fitting the attribute signals living on irregular geometric structures. Existing methods cannot achieve compact multiscale representation for high-fidelity reconstruction using the handcrafted transforms or deep learning-based techniques. In this paper, we propose a novel geometry-aware lifting-based multiscale network via spatial-channel lifting scheme for point cloud attribute compression. The proposed network cascades geometry-aware spatial lifting to reduce spatial redundancy by adaptively capturing irregular geometric structures and progressive channel lifting to progressively reduce channel-wise redundancy in multiscale representation. Furthermore, we design the split, predict, and update operations for geometry-aware spatial lifting to fully exploit the geometry information representing irregular structures. We develop geometry-aware adaptive split to equally split input points with significance scores indicating their dependencies, and propose geometry-aware cross-attention filtering for the predict and update operations for decorrelation based on geometry information. To our best knowledge, this paper achieves the first lifting-based learned transform for point cloud compression that enjoys reversibility guarantees of multiscale representation to enhance rate-distortion performance. Experimental results show that the proposed framework achieves state-of-the-art performance on extensive point cloud datasets, and outperforms latest MPEG G-PCC standard and most recent deep learning based methods.