DualRecon: Building 3D Reconstruction from Dual-View Re-mote Sensing Images

Large-scale and rapid 3D reconstruction of urban areas holds significant practical value. Recently, methods that reconstruct buildings from off-nadir imagery have gained attention for their po-tential to meet the demand for large-scale, time-sensitive reconstruction applications. These methods typically estimate the building height and footprint position by extracting building roof and the roof-to-footprint offset within a single off-nadir image. However, the reconstruction ac-curacy of these methods is primarily constrained by two issues: first, errors in the single-view building detection, and second, the inaccurate extraction of offsets, which is often a consequence of these detection errors as well as interference from shadow occlusion. To address these chal-lenge, we propose DualRecon, a method for 3D building reconstruction from heterogeneous dual-view remote sensing imagery. In contrast to single-image detection methods, DualRecon achieves more accurate 3D information extraction for reconstruction by fusing and correlating building information across different views. This success can be attributed to three key ad-vantages of DualRecon. First, DualRecon fuses the two input views and extracts building objects based on the fused image features, thereby improving the accuracy of building detection and localization. Second, compared to the roof-to-footprint offset, the disparity offset of the same rooftop between different views is less affected by interference from shadows and occlusions. Our method leverages this disparity offset to determine building height, which enhances the accuracy of height estimation. Third, we designed DualRecon with a three-branch architecture to be op-timally tailored for the dual-view 3D information extraction task. Third, we designed DualRecon with a three-branch architecture to be optimally tailored for the dual-view 3D information ex-traction task. Moreover, this paper introduces BuildingDual—the first large-scale dual-view 3D building reconstruction dataset. It comprises 3,789 image pairs containing 288,787 building in-stances, where each instance is annotated with its respective roofs in both views, roof-to-footprint offset, footprint, and the disparity offset of the roof. Experiments on this dataset demonstrate that DualRecon achieves more accurate reconstruction results than existing methods when perform-ing 3D building reconstruction from dual-view remote sensing imagery.