BiOcc: Bidirectional Context Modeling for Semantic Occupancy Compression

3D semantic occupancy has emerged as a unified representation of 3D scenes, with broad applications in scene generation, autonomous navigation, and collaborative perception. Nevertheless, transmitting dense 3D occupancy representations requires substantial bandwidth overhead, motivating efficient compression of both geometry and semantic labels. A natural approach is to treat semantic labels as point attributes and adapt existing point cloud codecs accordingly. Unlike conventional appearance attributes, semantic labels are spatially coherent and closely coupled with geometry, making them potentially informative for geometry coding. However, leveraging semantic information as context for geometry coding remains largely underexplored. To address this, we propose BiOcc, a bidirectional semantic occupancy compression framework that exploits early-decoded semantics for subsequent geometry coding and reconstructed geometry for finer-scale semantic coding. For semantics to geometry, label relative spatial context captures the arrangement of the same label neighbors, while the semantic category context encodes the current node's label. The semantic representations are combined with the causal geometry context to improve the prediction of occupancy patterns. For geometry to semantics, the local geometry context is combined with the available semantic context to predict label probabilities on occupied voxels. We further analyze the potential coding gain from semantic conditioning and show how semantic coarsening can reduce the geometry relevant information available for occupancy prediction. Extensive experiments on the outdoor nuScenes-Occ and indoor ScanNet-Occ datasets demonstrate that BiOcc achieves state-of-the-art performance in geometry and semantic compression.