4DR360: State Reasoning for Joint 3D Detection and Occupancy Prediction in 4D Radar-Camera Full-Scene Perception
Reliable 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 \method, a 4D radar-camera framework for 360$^\circ$ full-scene perception, which models semantic occupancy as a persistent scene state rather than a terminal output. \method{} 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.
4DR360:结合4D雷达和相机的360度全场景感知,优化3D检测和占用预测
- 核心方法
- 提出4DR360框架,通过跨模态状态推理,将占用状态作为持续的场景状态建模,包括状态引导的BEV增强和多普勒引导的时序融合
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 现有雷达-相机融合方法主要关注检测优化,未能充分实现3D检测与占用预测之间的强交互
- 关键实验
- 实验涵盖准确性、鲁棒性、消融实验和效率测试
- 主要贡献
- 扩展ManTruckScenes数据集,增加卫星地图生成的占用标签,并与OmniHD-Scenes结合,建立统一的多任务评估框架
- 意义与局限
- 提高了基于雷达的多任务学习系统的整体性能和鲁棒性,但可能面临计算资源的更高需求