BePo: Dual Representation for 3D Occupancy Prediction

3D occupancy infers fine-grained 3D geometry and semantics which is critical for autonomous driving. Most existing approaches carry high compute costs, requiring dense 3D feature volume and cross-attention to effectively aggregate information. More efficient methods adopt Bird's Eye View (BEV) or sparse points as scene representation leading to much reduced runtime. However, BEV struggles with sma…