{"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/mitigating-trade-off-stream-and-query-guided","title":"Mitigating Trade-off: Stream and Query-guided Aggregation for Efficient and Effective 3D Occupancy Prediction","arxiv_id":"2503.22087","date":"2025-03-28","proceeding":null,"authors":["Seokha Moon","Janghyun Baek","Giseop Kim","Jinkyu Kim","Sunwook Choi"],"abstract":"3D occupancy prediction has emerged as a key perception task for autonomous driving, as it reconstructs 3D environments to provide a comprehensive scene understanding. Recent studies focus on integrating spatiotemporal information obtained from past observations to improve prediction accuracy, using a multi-frame fusion approach that processes multiple past frames together. However, these methods struggle with a trade-off between efficiency and accuracy, which significantly limits their practicality. To mitigate this trade-off, we propose StreamOcc, a novel framework that aggregates spatio-temporal information in a stream-based manner. StreamOcc consists of two key components: (i) Stream-based Voxel Aggregation, which effectively accumulates past observations while minimizing computational costs, and (ii) Query-guided Aggregation, which recurrently aggregates instance-level features of dynamic objects into corresponding voxel features, refining fine-grained details of dynamic objects. Experiments on the Occ3D-nuScenes dataset show that StreamOcc achieves state-of-the-art performance in real-time settings, while reducing memory usage by more than 50% compared to previous methods.","url_abs":"https://arxiv.org/abs/2503.22087v1","url_pdf":"https://arxiv.org/pdf/2503.22087v1.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":"mitigating-trade-off-stream-and-query-guided","repo_url":"https://github.com/moonseokha/streamocc","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"scene-understanding","task_name":"Scene Understanding"}],"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}