SoDPCC: Shared-Octree Coding for Dynamic LiDAR Point Cloud Compression

Dynamic LiDAR compression aims to compactly encode continuously evolving 3D scenes, while existing codecs mainly exploit temporal redundancy by compressing consecutive frames in a frame-wise manner. However, this paradigm leaves temporal correlation only implicitly reflected in the statistical similarity between adjacent encoded frames, which limits the effective exploitation of temporal information. To address this limitation, we propose SoDPCC, a learned LiDAR stream compression framework comprising a shared octree and inter-frame presence states, which respectively reduce spatial symbols through hierarchical geometry sharing and temporal uncertainty through compact state modeling. Specifically, SoDPCC constructs a shared octree for two consecutive frames to remove duplicated geometry across hierarchy levels, encodes each leaf’s inter-frame presence as a compact state, and models the resulting occupancy and state streams with dedicated Transformer-based entropy models. For temporal-state coding, we design Progressive Grouped State Factorization to progressively expose denser decoded state cues for subsequent prediction. We further propose Full-Spatial State-Geometry Coupling to integrate these cues with spatially aligned geometry features and propagate the resulting state-aware context over the complete leaf sequence. The experimental results show that our SoDPCC outperforms state-of-the-art methods, achieving 41.78% and 49.43% BD-Rate gains on SemanticKITTI and Ford datasets, respectively. Moreover, SoDPCC preserves competitive vehicle-detection performance on reconstructed LiDAR point clouds, demonstrating its practical utility for downstream perception.