Title: GAP3D: Geometry-Aware Adaptive Planar Representations for 3D Occupancy Prediction
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: 3d occupancy prediction, geometry-aware learning

Abstract: We address the problem of 3D occupancy prediction from multi-view images, a task central to autonomous driving and embodied perception. Conventional methods typically employ fixed 3D voxel grids, which provide structured coverage of the entire scene but scale poorly in memory and computation as resolution increases. Sparse 3D representations, by contrast, improve efficiency  by focusing only on occupied regions, but risk missing critical areas and often converge slowly. To overcome these limitations, we propose $\textbf{Geometry-Aware Adaptive Planar Representations (GAP3D)}$, a new adaptive 3D representation that combines the comprehensive coverage of voxel grids with the efficiency of sparse methods. GAP3D has two key components: (1) $\textbf{Adaptive plane representations}$, where planes sequentially partition vertical space via a stick-breaking process, concentrating representational capacity in regions with high occupancy likelihood; and (2) $\textbf{Geometry-aware splatting}$, which lifts the plane features into the full 3D occupancy volume through a differentiable Gaussian kernel, producing dense, spatially consistent predictions while preserving scene geometry. To further guide plane placement during early training, we introduce a height regularization loss that encourages alignment with scene structure. Experiments on the Occ3D-nuScenes benchmark demonstrate that GAP3D achieves state-of-the-art performance while significantly reducing memory and computation compared to existing approaches.

TL;DR: GAP3D is a geometry-aware adaptive planar representation for 3D occupancy prediction.