Large-scale spatio-temporal attention based entropy model for point cloud compression

In octree-based point cloud compression, an effective entropy model is required to reduce the final code length. The large-scale context provides more references and improves the accuracy of the entropy coder. In this paper, we propose a learning-based entropy model to exploit the large-scale spatio-temporal context for dynamic point cloud compression. We design an octree-based context structure which substantially expands the context. To extract powerful features from the informative large-scale context, we propose a geometry-aware graph-based feature extraction model. Furthermore, we present a spatio-temporal attention mechanism to discover dependencies within the large-scale context. Extensive experiments demonstrate that the proposed method achieves state-of-the-art compression performance.