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

作者：Ruizhe Shao, Hao Chen, Jun Li, Mengyu Ma, Chun Du · 发表于：Remote Sensing · 年份：2025 · DOI：10.3390/rs17233793 · 被引用次数：1 · 研究领域：Remote Sensing and LiDAR Applications、Automated Road and Building Extraction、Remote-Sensing Image Classification

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 potential 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 accuracy 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 challenges, 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 advantages 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 an...