SG-BEV: Satellite-Guided BEV Fusion for Cross-View Semantic Segmentation
Junyan Ye Qiyan Luo Jinhua Yu Huaping Zhong Zhimeng Zheng Conghui He Weijia Li
This paper aims at achieving fine-grained building attribute segmentation in
a cross-view scenario, i.e., using satellite and street-view image pairs. The
main challenge lies in overcoming the significant perspective differences
between street views and satellite views. In this work, we introduce SG-BEV, a
novel approach for satellite-guided BEV fusion for cross-view semantic
segmentation. To overcome the limitations of existing cross-view projection
methods in capturing the complete building facade features, we innovatively
incorporate Bird's Eye View (BEV) method to establish a spatially explicit
mapping of street-view features. Moreover, we fully leverage the advantages of
multiple perspectives by introducing a novel satellite-guided reprojection
module, optimizing the uneven feature distribution issues associated with
traditional BEV methods. Our method demonstrates significant improvements on
four cross-view datasets collected from multiple cities, including New York,
San Francisco, and Boston. On average across these datasets, our method
achieves an increase in mIOU by 10.13% and 5.21% compared with the
state-of-the-art satellite-based and cross-view methods. The code and datasets
of this work will be released at https://github.com/yejy53/SG-BEV.