Treating occupancy prediction as an evolving scene state rather than a final output enables better radar-camera fusion for autonomous driving, improving both object detection and dense scene understanding simultaneously.
This paper presents 4DR360, a radar-camera fusion system for autonomous driving that jointly detects 3D objects and predicts scene occupancy in 360° views. Unlike prior methods that treat these tasks separately, it models occupancy as a persistent scene state that's refined across processing stages, using radar's Doppler information to maintain temporal consistency.