Learning-based Intra-Prediction For Point Cloud Attribute Transform Coding

The attached attributes of each point in point cloud are fairly valuable but aggravate the burden for storage and transmission. In this paper, we propose a learning-based introprediction method for region adaptive hierarchical transform (RAHT) to compress point cloud attributes efficiently. First, we design an adaptive neighbor selection (ANS) module to produce the most correlated neighbors for child nodes. Then, the correlated neighbors obtained by ANS, the corresponding distance weights, and some additional auxiliary information are concatenated and fed to a multi-layer perception (MLP) based network to estimate the child node attributes precisely. Besides, residual learning is introduced to accelerate the network convergence. The predicted child node attributes are finally transformed by RAHT, and the residual of transform coefficients are then quantized and entropy coded. Experimental results demonstrate that our proposed methods can significantly improve attribute coding efficiency with average 10.2% BD-Rate gains compared with MPEG G-PCC reference software TMC13v14.0 on MPEG PCC dataset.