Efficient Hierarchical Entropy Model for Learned Point Cloud Compression

Learning an accurate entropy model is a fundamental way to remove the redundancy in point cloud compression. Recently, the octree-based auto-regressive entropy model which adopts the self-attention mechanism to explore dependencies in a large-scale context is proved to be promising. However, heavy global attention computations and auto-regressive contexts are inefficient for practical applications. To improve the efficiency of the attention model, we propose a hierarchical attention structure that has a linear complexity to the context scale and maintains the global receptive field. Furthermore, we present a grouped context structure to address the serial decoding issue caused by the auto-regression while preserving the compression performance. Experiments demonstrate that the proposed entropy model achieves superior rate-distortion performance and significant decoding latency reduction compared with the state-of-the-art large-scale auto-regressive entropy model.

1. Introduction

Point cloud is a fundamental data structure to represent 3D scenes. It has been widely applied in 3D vision systems such as autonomous driving and immersive applications. The large-scale point cloud typically contains millions of points [36]. It is challenging to store and transmit such massive data. Hence, efficient point cloud compression that reduces memory footprints and transmission bandwidth is necessary to develop practical point cloud applications.