CRC-DPCGC: Conditional Residual Coding for Dynamic Point Cloud Geometry Compression

Dynamic point cloud compression faces significant challenges due to the inherent unstructured and sparse nature of point clouds, requiring efficient modeling of temporal context information. Current approaches often fall short in effectively capturing and utilizing inter-frame dependencies. This paper presents a novel conditional residual coding technique for dynamic point cloud geometry compression, specifically designed to address this challenge. Our method exploits spatial-temporal features by a predictor module and a Transformer-based entropy model. The predictor module transfers features from previous point cloud frames to the current frame, efficiently generating residual features. The Transformer entropy model employs a conditional coding strategy, utilizing cross-attention between the current frame and its predecessors, leading to a more precise bit estimation. Extensive experimental results demonstrate that our proposed method outperforms existing dynamic point cloud geometry compression techniques, including rule-based and learningbased approaches, showcasing its effectiveness in capturing and utilizing temporal information.