{"id":39203,"date":"2026-08-21T15:31:36","date_gmt":"2026-08-21T15:31:36","guid":{"rendered":"https:\/\/lamarr-institute.org\/publication\/oc-sop-enhancing-vision-based-3d-semantic-occupancy-prediction-by-object-centric-awareness\/"},"modified":"2026-09-17T11:21:57","modified_gmt":"2026-09-17T11:21:57","slug":"oc-sop-enhancing-vision-based-3d-semantic-occupancy-prediction-by-object-centric-awareness","status":"publish","type":"publication","link":"https:\/\/lamarr-institute.org\/de\/publication\/oc-sop-enhancing-vision-based-3d-semantic-occupancy-prediction-by-object-centric-awareness\/","title":{"rendered":"{OC}-{SOP}: Enhancing Vision-Based 3D Semantic Occupancy Prediction by Object-Centric Awareness"},"content":{"rendered":"<p>Autonomous driving perception faces significant challenges due to occlusions and incomplete scene data in the environment. To overcome these issues, the task of semantic occupancy prediction ({SOP}) is proposed, which aims to jointly infer both the geometry and semantic labels of a scene from images. However, conventional camera-based methods typically treat all categories equally and primarily rely on local features, leading to suboptimal predictions, especially for dynamic foreground objects. To address this, we propose Object-Centric {SOP} ({OC}-{SOP}), a framework that integrates high-level object-centric cues extracted via a detection branch into the semantic occupancy prediction pipeline. This object-centric integration significantly enhances the prediction accuracy for foreground objects and achieves state-of-the-art performance among all categories on {SemanticKITTI}.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Autonomous driving perception faces significant challenges due to occlusions and incomplete scene data in the environment. To overcome these issues, the task of semantic occupancy prediction ({SOP}) is proposed, which aims to jointly infer both the geometry and semantic labels of a scene from images. However, conventional camera-based methods typically treat all categories equally and primarily rely on local features, leading to suboptimal predictions, especially for dynamic foreground objects. To [&hellip;]<\/p>\n","protected":false},"author":14,"featured_media":0,"template":"","meta":{"_acf_changed":false,"footnotes":""},"publication-type":[32],"class_list":["post-39203","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\/39203","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\/39203\/revisions"}],"wp:attachment":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/media?parent=39203"}],"wp:term":[{"taxonomy":"publication-type","embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication-type?post=39203"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}