PDE-based Progressive Prediction Framework for Attribute Compression of 3D Point Clouds

In recent years, the diffusion-based image compression scheme has achieved significant success, which inspires us to use diffusion theory to employ the diffusion model for point cloud attribute compression. However, the relevant existing methods cannot be used to deal with our task due to the irregular structure of point clouds. To handle this, we propose the partial differential equation (PDE) based progressive prediction framework for attribute compression of 3D point clouds. Firstly, we propose a PDE-based prediction module, which performs prediction by optimizing attribute gradients, allowing the geometric distribution of adjacent areas to be fully utilized and explaining the weighting method for prediction. Besides, we propose a low-complexity method for calculating partial derivative operations on point clouds to address the uncertainty of neighbor occupancy in three-dimensional space. In the proposed prediction framework, we design a two-layer level of detail (LOD) structure, where the attribute information in the high level is used for interpolating the low level by edge-enhancing anisotropic diffusion (EED) to infer local features from the high-level information. After the diffusion-based interpolation, we design a texture-wise prediction method making use of interpolated values and texture information. Experiment results show that our proposed framework achieves an average of 12.00% BD-rate reduction and 1.59% bitrate saving compared with Predlift (PLT) under attribute near-lossless and attribute lossless conditions, respectively. Furthermore, additional experiments demonstrate our proposed scheme has better texture preservation and subjective quality.