Octree-STCM: Octree-Based Spatio-Temporal Context Model for Lossless Geometry Compression of Dynamic Point Cloud

Deep learning approaches have demonstrated remarkable effectiveness in point cloud geometry compression. However, existing octree-based methods face limitations due to insufficient contextual utilization within temporal sequences of dynamic point clouds. This paper proposes a spatio-temporal context model under an octree structure to enhance lossless compression of dynamic point cloud geometry. Firstly, a context extraction module is employed to capture the intra-contexts based on spatial correlations and the inter-contexts based on temporal dependencies. Subsequently, a context network employing 3D convolutional layers and fully connected layers is designed to extract spatio-temporal features from various contexts. After the context features integration, a multilayer perceptron is used to approximate the probability distribution of the occupancy symbol. The derived probability distributions finally optimize the arithmetic coding efficiency. Experimental results demonstrate that the proposed method outperforms the state-of-the-art octree-based approaches across multiple benchmark datasets.