On Block Prediction For Learning-Based Point Cloud Compression

Point clouds are among popular visual representations for immersive media. However, the vast amount of information generated during their acquisition requires effective compression for practical applications. Although relevant activities from standardization bodies have led to state-of-the-art compression using conventional methods, learning-based encoders have recently emerged as promising solutions with comparable performance while offering additional attractive features. Yet, there is still a large unexplored space for research that can lead to further advances. In this paper, we propose a block prediction module for bit-rate reduction of geometry-only point clouds. Our method exploits spatial redundancies at the decoding stage between block partitions in the point cloud, and predicts a query block using Generative Adversarial Networks. Results show performance improvements of the objective metrics at low bit-rates, after integration in a baseline auto-encoder architecture.