DeepRAHT: Learning Predictive RAHT for Point Cloud Attribute Compression

Regional Adaptive Hierarchical Transform (RAHT) is an effective point cloud attribute compression (PCAC) method. However, its application in deep learning lacks research. This paper proposes an end-to-end RAHT framework for lossy PCAC based on the sparse tensor, called DeepRAHT. The RAHT transform is performed within the end-to-end reconstruction process, without requiring manual RAHT for pre-processing. We also introduce the predictive RAHT to reduce bitrates and design a learning-based prediction model to enhance the performance. Moreover, we devise a bitrate proxy that applies run-length coding to entropy coding, achieving seamless variable-rate coding and improving the robustness. DeepRAHT is a reversible and distortion-controllable framework, ensuring its lower bound performance and offering significant application potential. The experiments demonstrate that DeepRAHT is a high-performance, faster, and more robust framework than the baseline solutions.