Rethinking Temporal Fusion with a Unified Gradient Descent View for 3D Semantic Occupancy Prediction

Dubing Chen, Huan Zheng, Jin Fang, Xingping Dong, Xianfei Li, Wenlong Liao, Tao He, Pai Peng, Jianbing Shen; Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR), 2025, pp. 1505-1515

We present GDFusion, a temporal fusion method for vision-based 3D semantic occupancy prediction (VisionOcc). GDFusion opens up the underexplored aspects of temporal fusion within the VisionOcc framework, with a focus on both temporal cues and fusion strategies. It systematically examines the entire VisionOcc pipeline, identifying three fundamental yet previously overlooked temporal cues: scene-level consistencies, motion calibration, and geometric complementation. These cues capture diverse facets of temporal evolution and provide distinctive contributions across various modules in the general VisionOcc framework. To effectively fuse temporal signals across heterogeneous representations, we propose a novel fusion strategy by reinterpreting the formulation of vanilla RNNs. This reinterpretation leverages gradient descent on features to unify the integration of diverse temporal information, seamlessly embedding the proposed temporal cues into the network. Extensive experiments on nuScenes demonstrate that GDFusion significantly outperforms established baselines, achieving 2.2%--4.7% mIoU improvement while reducing memory consumption by 30%--72%. Codes are available at https://github.com/cdb342/GDFusion.