{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/out-of-distribution-semantic-occupancy","title":"Out-of-Distribution Semantic Occupancy Prediction","arxiv_id":"2506.21185","date":"2025-06-26","proceeding":null,"authors":["Yuheng Zhang","Mengfei Duan","Kunyu Peng","Yuhang Wang","Ruiping Liu","Fei Teng","Kai Luo","Zhiyong Li","Kailun Yang"],"abstract":"3D Semantic Occupancy Prediction is crucial for autonomous driving, providing a dense, semantically rich environmental representation. However, existing methods focus on in-distribution scenes, making them susceptible to Out-of-Distribution (OoD) objects and long-tail distributions, which increases the risk of undetected anomalies and misinterpretations, posing safety hazards. To address these challenges, we introduce Out-of-Distribution Semantic Occupancy Prediction, targeting OoD detection in 3D voxel space. To fill the gaps in the dataset, we propose a Synthetic Anomaly Integration Pipeline that injects synthetic anomalies while preserving realistic spatial and occlusion patterns, enabling the creation of two datasets: VAA-KITTI and VAA-KITTI-360. We introduce OccOoD, a novel framework integrating OoD detection into 3D semantic occupancy prediction, with Voxel-BEV Progressive Fusion (VBPF) leveraging an RWKV-based branch to enhance OoD detection via geometry-semantic fusion. Experimental results demonstrate that OccOoD achieves state-of-the-art OoD detection with an AuROC of 67.34% and an AuPRCr of 29.21% within a 1.2m region, while maintaining competitive occupancy prediction performance. The established datasets and source code will be made publicly available at https://github.com/7uHeng/OccOoD.","url_abs":"https://arxiv.org/abs/2506.21185v1","url_pdf":"https://arxiv.org/pdf/2506.21185v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"out-of-distribution-semantic-occupancy","repo_url":"https://github.com/7uheng/occood","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"3d-semantic-occupancy-prediction","task_name":"3D Semantic Occupancy Prediction"},{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"prediction","task_name":"Prediction"}],"methods":[{"method_slug":"focus","method_name":"Focus"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}