SA-Occ: Satellite-Assisted 3D Occupancy Prediction in Real World

Chen Chen; Zhirui Wang; Taowei Sheng; Yi Jiang; Yundu Li; Peirui Cheng; Luning Zhang; Kaiqiang Chen; Yanfeng Hu; Xue Yang; Xian Sun

arXiv · 2503.16399

SA-Occ: Satellite-Assisted 3D Occupancy Prediction in Real World

Chen Chen·Zhirui Wang·Taowei Sheng·Yi Jiang·Yundu Li·Peirui Cheng·Luning Zhang·Kaiqiang Chen·Yanfeng Hu·Xue Yang·Xian Sun

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Abstract

Existing vision-based 3D occupancy prediction methods are inherently limited in accuracy due to their exclusive reliance on street-view imagery, neglecting the potential benefits of incorporating satellite views. We propose SA-Occ, the first Satellite-Assisted 3D occupancy prediction model, which leverages GPS & IMU to integrate historical yet readily available satellite imagery into real-time applications, effectively mitigating limitations of ego-vehicle perceptions, involving occlusions and degraded performance in distant regions. To address the core challenges of cross-view perception, we propose: 1) Dynamic-Decoupling Fusion, which resolves inconsistencies in dynamic regions caused by the temporal asynchrony between satellite and street views; 2) 3D-Proj Guidance, a module that enhances 3D feature extraction from inherently 2D satellite imagery; and 3) Uniform Sampling Alignment, which aligns the sampling density between street and satellite views. Evaluated on Occ3D-nuScenes, SA-Occ achieves state-of-the-art performance, especially among single-frame methods, with a 39.05% mIoU (a 6.97% improvement), while incurring only 6.93 ms of additional latency per frame. Our code and newly curated dataset are available at https://github.com/chenchen235/SA-Occ.

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Chen Chen, Zhirui Wang, Taowei Sheng, Yi Jiang, Yundu Li, Peirui Cheng, Luning Zhang, Kaiqiang Chen, Yanfeng Hu, Xue Yang, Xian Sun. 2025-03-20. SA-Occ: Satellite-Assisted 3D Occupancy Prediction in Real World. https://arxiv.org/abs/2503.16399

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