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There is an urgent need from various multimedia applications to efficiently compress point clouds. The Moving Picture Experts Group has released a standard platform called geometry-based point cloud compression (G-PCC). However, its k-nearest neighbor (k-NN) based attribute prediction has limited efficiency for point clouds with rich texture and directional information. To overcome this problem, we propose a texture-aware attribute predictive coding framework in a point cloud diffusion model. In our work, attribute intra prediction is solved as a diffusion-based interpolation problem, and a general attribute predictor is developed. It is theoretically proven that G-PCC k-NN based predictor is a degraded case of the proposed diffusion-based solution. First, a point cloud is represented as two levels of details with seeds as the inpainting mask and non-seed points to be predicted. Second, we design point cloud partial difference operators to perform energy-minimizing attribute inpainting from seeds to unknowns. Smooth attribute interpolation can be achieved via an iterative diffusion process, and an adaptive early termination is proposed to reduce complexity. Third, we propose a structure-adaptive attribute predictive coding scheme, where edge-enhancing anisotropic diffusion is employed to perform texture-aware attribute prediction. Finally, attributes of seeds are beforehand encoded and prediction residuals of left points are progressively encoded into bitstream. Experiments show the proposed scheme surpasses the state-of-the-art by an average of 14.14%, 17.52%, and 17.87% BD-BR gains on the coding of Y, U, and V components, respectively. Subjective results on attribute reconstruction quality also verify the advantage of our scheme.

There is a critical requirement for efficiently compressing point cloud geometries representing 3-D moving objects in various applications. The Moving Picture Experts Group 3-D Graphics coding group (MPEG 3DG) set up an interexploration model for geometry-based point cloud compression (G-PCC interEM). However, the block-matching motion compensation (MC) scheme with a translational motion model has limited ability to handle dense point clouds with complex local motions. To overcome this problem, we propose a progressive nonrigid MC framework for point cloud geometry compression, where the point cloud registration technique is introduced and tailored with our designed rate–distortion cost. In the coarse-grained stage, a point cloud is represented as deformable point patches, and the patchwise nonrigid motion estimation (ME) task is formulated as a registration-based optimization problem that can be efficiently solved by the majorization–minimization (MM) method. In the fine-grained stage, we propose a block-based motion refinement to enhance the estimated motion field in the local region, followed by a multihypothesis MC scheme enabling smooth reference reconstruction with patchwise deformation and blockwise refined motions. Experiments demonstrate our proposed scheme outperforms several competitive platforms in terms of both coding performance and compensation quality. Compared with G-PCC interEM, our proposed framework achieves significant bitrate savings, i.e., 4.71% (32 frames) and 4.22% (200 frames), for point cloud lossless geometry compression.