4DR360: State Reasoning for Joint 3D Detection and Occupancy Prediction in 4D Radar-Camera Full-Scene Perception

Autonomous driving requires perceiving both objects and the surrounding environment, but existing radar-camera fusion methods focus mainly on detection rather than joint scene understanding. This paper proposes a framework treating semantic occupancy as an evolving internal state rather than a final output, using specialized modules to strengthen spatial and temporal feature fusion from 4D radar and camera data. The authors also extend existing driving datasets with new occupancy labels. Applications include more robust perception systems for self-driving cars, particularly in adverse conditions where radar's reliability complements camera limitations, improving both object detection and scene-layout understanding simultaneously. Authors: Xiaokai Bai, Lianqing Zheng, Runwei Guan, Songkai Wang, Siyuan Cao, Hui-liang Shen Paper: https://arxiv.org/abs/2607.09629v1