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 this https URL

该论文介绍了一种用于卫星和街景图像对的细粒度建筑属性分割的新方法，通过引入鸟瞰图方法解决了传统方法中建筑物立面特征获取的局限性，并通过新的卫星引导的投影模块优化了传统方法中特征分布不均匀的问题。该方法在来自多个城市的四个跨视图数据集上取得了显著的改进，与最先进的基于卫星和跨视图的方法相比，平均 mIOU 提高了 10.13% 和 5.21%。