Joint Geometry-Attribute Point Cloud Compression with Spatial Context Mining and Dual-Class Attribute Loss

Joint geometry-attribute point cloud compression is formulated to compress both geometry coordinates and their corresponding attributes within a single model, thereby yielding the benefit of reduced computation and enhancing the feasibility of joint geometry-attribute optimization. However, previous single-model approaches have been hampered by inadequate utilization of local context, thereby retaining spatial redundancy. To effectively characterize the spatial correlation conditioned on the neighborhood, we propose a novel spatial context model that features group division for two-stage parallel processing and incorporates a local attention-based spatial context mining module. Furthermore, for joint compression tasks, geometry distortion results in the absence of references for misclassified voxels, consequently preventing one-to-one matching between the reference and the reconstruction for attribute loss computation. To address this issue, we propose a dual-class attribute loss that enhances guidance by searching for the nearest neighbors of misclassified voxels and utilizing them as references. Experiments demonstrate that the proposed joint compressor achieves state-of-the-art rate-distortion performance compared to existing single-model methods and yields a competitive result against multiple-model solutions.