{"id":39204,"date":"2026-08-21T15:31:36","date_gmt":"2026-08-21T15:31:36","guid":{"rendered":"https:\/\/lamarr-institute.org\/publication\/swa-sop-spatially-aware-window-attention-for-semantic-occupancy-prediction-in-autonomous-driving\/"},"modified":"2026-09-17T11:21:57","modified_gmt":"2026-09-17T11:21:57","slug":"swa-sop-spatially-aware-window-attention-for-semantic-occupancy-prediction-in-autonomous-driving","status":"publish","type":"publication","link":"https:\/\/lamarr-institute.org\/de\/publication\/swa-sop-spatially-aware-window-attention-for-semantic-occupancy-prediction-in-autonomous-driving\/","title":{"rendered":"{SWA}-{SOP}: Spatially-aware Window Attention for Semantic Occupancy Prediction in Autonomous Driving"},"content":{"rendered":"<p>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.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>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 [&hellip;]<\/p>\n","protected":false},"author":14,"featured_media":0,"template":"","meta":{"_acf_changed":false,"footnotes":""},"publication-type":[32],"class_list":["post-39204","publication","type-publication","status-publish","hentry","publication-type-inproceedings"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/39204","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication"}],"about":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/types\/publication"}],"author":[{"embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/users\/14"}],"version-history":[{"count":0,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/39204\/revisions"}],"wp:attachment":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/media?parent=39204"}],"wp:term":[{"taxonomy":"publication-type","embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication-type?post=39204"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}