{SWA}-{SOP}: Spatially-aware Window Attention for Semantic Occupancy Prediction in Autonomous Driving

Perception systems in autonomous driving rely on sensors such as {LiDAR} and cameras to perceive the 3D environment. However, due to occlusions and data sparsity, these sensors often fail to capture complete information. Semantic Occupancy Prediction ({SOP}) addresses this challenge by inferring both occupancy and semantics of unobserved regions. Existing transformer-based {SOP} methods lack explicit modeling of spatial structure in attention computation, resulting in limited geometric awareness and poor performance in sparse or occluded areas. To this end, we propose Spatially-aware Window Attention ({SWA}), a novel mechanism that incorporates local spatial context into attention. {SWA} significantly improves scene completion and achieves state-of-the-art results on {LiDAR}-based {SOP} benchmarks. We further validate its generality by integrating {SWA} into a camera-based {SOP} pipeline, where it also yields consistent gains across modalities.