Hierarchical Distortion Learning for Fast Lossy Compression of Point Clouds

The growth of 3D point cloud applications requires efficient compression techniques for high-quality and low-latency services. Recently, learning-based point cloud compression models have made significant progress. However, geometric distortion resulting from downsampling limits the feature depth within large-scale point clouds, thereby constraining the receptive field and suppressing the redundant removal. Moreover, the issues of computational efficiency and reconstruction quality still persist in the compression of large-scale point clouds. To address these challenges, we propose a hierarchical distortion learning framework for end-to-end lossy compression of point clouds. First, we design a feature residual compression module to efficiently transmit shallow semantics between the encoder and the decoder, which enables a lightweight design of our framework. Second, we introduce a geometry residual compression module to progressively complement the reconstruction distortion, avoiding the accumulation of geometric distortion. By integrating these two modules and employing sufficient downsampling processes, we develop a high-performance framework with a significantly enlarged receptive field and low computational cost. Extensive experiments demonstrate that our method achieves state-of-the-art performance in geometry lossy compression, while delivering competitive performance in joint geometry and color lossy compression with fast running speed.