Regional Adaptive Hierarchical Transform (RAHT) is an effective point cloud attribute compression (PCAC) method. However, its application in deep learning lacks research. In this paper, we propose an end-to-end RAHT framework for lossy PCAC based on the sparse tensor, called DeepRAHT. The RAHT transform is performed within the learning reconstruction process, without requiring manual RAHT for preprocessing. We also introduce the predictive RAHT to reduce bitrates and design a learning-based prediction model to enhance performance. Moreover, we devise a bitrate proxy that applies run-length coding to entropy model, achieving seamless variable-rate coding and improving 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 solution than the baseline methods. Project Page: https://github.com/zb12138/DeepRAHT.

翻译：区域自适应分层变换（RAHT）是一种有效的点云属性压缩方法。然而，其在深度学习中的应用尚缺乏研究。本文提出了一种基于稀疏张量的端到端RAHT框架，用于有损点云属性压缩，称为DeepRAHT。RAHT变换在学习重建过程中执行，无需手动进行RAHT预处理。我们还引入了预测性RAHT以降低码率，并设计了一个基于学习的预测模型以提升性能。此外，我们设计了一种将游程编码应用于熵模型的码率代理，实现了无缝可变码率编码并提高了鲁棒性。DeepRAHT是一个可逆且失真可控的框架，确保了其性能下界，并具有显著的应用潜力。实验表明，与基线方法相比，DeepRAHT是一种高性能、更快速且更鲁棒的解决方案。项目页面：https://github.com/zb12138/DeepRAHT。