[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82435-en":3,"doc-seo-82435-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82435,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","4DR360° State Reasoning for Joint 3D Detection and Occupancy Prediction in 4D Radar-Camera Full-Scene Perception","Reliable autonomous driving depends on full-scene perception that unifies foreground object understanding with dense semantic scene layout. While 4D millimeter-wave radar is robust and affordable, its sparse returns necessitate radar-camera fusion for comprehensive perception. This work introduces 4DR360°, a 4D radar-camera framework that treats semantic occupancy as a persistent scene state, enabling cross-modal state reasoning. SBE strengthens intra-frame BEV features and DTF propagates state evidence temporally. Experiments extend ManTruckScenes and OmniHD-Scenes with unified joint detection-and-occupancy evaluation, reporting accuracy, robustness, ablations, and efficiency; code and labels are released after acceptance.","4DR360◦ : State Reasoning for Joint 3D Detection and Occupancy Prediction  \nin 4D Radar-Camera Full-Scene Perception  \nXiaokai Bai 1 , Lianqing Zheng2 , Runwei Guan3 , Songkai Wang 1 , Siyuan Cao 1 , Hui-liang Shen 1  \n1 College of Information Science and Electronic Engineering, Zhejiang University.  \n2 School of Automotive Studies, Tongji University.  \n3Thrust of Artificial Intelligence, Hong Kong University of Science and Technology.  \n[shawnnnkb@gmail.com](shawnnnkb@gmail.com)  \narXiv :2607 .09629v1 [ cs .CV] 10 Jul 2026  \nAbstract  \nReliable autonomous driving requires full-scene perception that couples foreground objects with dense semantic layout. Recently, 4D millimeter-wave radar has emerged as a robust and affordable sensor, yet its sparse returns make radar-camera fusion necessary for comprehensive scene understanding. Existing radar-camera methods mainly optimize detection, while dual-task systems usually decode boxes and occupancy with limited interaction. To address this gap and advance radar-based multi-task learning, we propose 4DR360◦ , a 4D radar-camera framework for 360◦ full-scene perception, which models semantic occupancy as a persistent scene state rather than a terminal output. 4DR360◦ follows a cross-modal state reasoning paradigm, where the occupancy state is modeled and propagated through stages for coarse-to-fine feature aggregation. Specifically, State-guided BEV Enhancement (SBE) strengthens intra-frame BEV representation, while Doppler-guided Temporal Fusion (DTF) preserves state evidence over longer temporal horizons. Beyond the model, we further extend ManTruckScenes with satellite-map-based generated occupancy labels and pair it with OmniHD-Scenes in a unified cross-dataset detection-and-occupancy protocol. The resulting experiments cover accuracy, robustness, ablation, and efficiency under one radar-camera multi-task evaluation framework. Code and labels will be released upon acceptance.  \nIntroduction  \nAutonomous driving requires 360◦ full-scene perception that jointly models foreground agents and surrounding scene layout. In this setting, 3D object detection estimates compact boxes for traffic participants (Liu et al. 2023; Bai et al. 2024, 2026c; Xia et al. 2026), whereas semantic occupancy prediction recovers dense free, occupied, and semantic voxel states around the ego vehicle (Cao and De Charette 2022; Li et al. 2023; Wei et al. 2023; Tian et al. 2023; Zheng et al. 2024) . These two tasks describe the same scene at different granularities. Detection focuses on instance-level localization, while occupancy supplies the spatial support and layout context in which those instances exist. Therefore, reliable full-scene perception requires a unified treatment of object reasoning and scene layout.  \nDriven by recent datasets and radar-camera perception methods, 4D millimeter-wave radar is becoming an  \nCopyright © 2026 . All rights reserved.  \n(d) 4DR360° (Ours) Cross-modal State Reasoning Multi-view Joint Task  \nFigure 1 . Comparison of representative 4D radar-camera paradigms. (a) SGDet3D is front-view and detection-only,(b) RCBEVDet extends detection to 360◦ perception,(c) Doracamom supports joint detection and occupancy prediction without explicit state reasoning, and (d) 4DR360◦ organizes 360◦ radar-camera multi-task perception through occupancy-state reasoning with SBE and DTF.  \nimportant sensing modality for autonomous driving (Palffyet al. 2022; Zheng et al. 2022; Paek, Kong, and Wijaya 2022; Zheng et al. 2023; Lin et al. 2024; Bai et al. 2024, 2026c; Xia et al. 2026) . It provides stable geometric and motion cues under challenging conditions. However, its sparse returns still require camera semantics for complete scene understanding. Recent radar-camera methods therefore improve bird’s-eye-view fusion, semantic-geometric interaction, sparse object representation, and temporal modeling (Zheng et al. 2023; Lin et al. 2024; Bai et al. 2024, 2026c; Xia et al. 2026) . Nevertheless, most o","cbCaibnNmLD2Ddmg","https://ap.wps.com/l/cbCaibnNmLD2Ddmg","pdf",2566619,1,9,"English","en",105,"# Abstract\n# Introduction\n# Related Work and Motivation\n# Framework Overview\n## Cross-modal state reasoning with SBE and DTF\n## Cross-dataset joint protocol (ManTruckScenes and OmniHD-Scenes)","[{\"question\":\"What problem does 4DR360° address in radar-camera full-scene perception?\",\"answer\":\"It targets the gap where radar-camera fusion systems mainly optimize detection, while occupancy and detection are not sufficiently coupled through interactive, state-aware reasoning for full-scene understanding.\"},{\"question\":\"How does 4DR360° model occupancy in the proposed framework?\",\"answer\":\"Occupancy is modeled as a persistent scene state rather than a terminal output, and the state is represented and propagated through stages for coarse-to-fine aggregation.\"},{\"question\":\"What roles do SBE and DTF play in the method?\",\"answer\":\"State-guided BEV Enhancement (SBE) strengthens intra-frame BEV representation, while Doppler-guided Temporal Fusion (DTF) preserves state evidence over longer temporal 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problem does 4DR360° address in radar-camera full-scene perception?","Question",{"text":75,"@type":76},"It targets the gap where radar-camera fusion systems mainly optimize detection, while occupancy and detection are not sufficiently coupled through interactive, state-aware reasoning for full-scene understanding.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does 4DR360° model occupancy in the proposed framework?",{"text":80,"@type":76},"Occupancy is modeled as a persistent scene state rather than a terminal output, and the state is represented and propagated through stages for coarse-to-fine aggregation.",{"name":82,"@type":73,"acceptedAnswer":83},"What roles do SBE and DTF play in the method?",{"text":84,"@type":76},"State-guided BEV Enhancement (SBE) strengthens intra-frame BEV representation, while Doppler-guided Temporal Fusion (DTF) preserves state evidence over longer temporal 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