Perspective-Invariant Matching of Aerial–Terrestrial Images Based on Multi-Coplanar Geometry for Building Modeling

Multi-view images captured from both aerial and terrestrial perspectives exhibit substantial variations in viewpoints, pronounced geometric distortions, and significant scale changes, posing considerable challenges for the integrated three-dimensional (3D) modeling of buildings within an aerial–terrestrial collaborative framework. To address these challenges, this paper introduces a perspective-invariant matching method for aerial–terrestrial images based on multi-coplanar geometry, specifically tailored for building modeling. The proposed method develops an innovative multi-perspective transformation model that iteratively extracts multiple coplanar geometric relationships from two-dimensional imagery using local matching correlations. Furthermore, it deduces the functional interrelations among these planes to construct a tailored multi-coplanar geometric model for local matching domains. Subsequently, the local imagery is segmented into subimages across various perspective spaces, and a deep matching graph neural network (GNN) is used to discern point and line structures within these images. By applying inverse perspective transformations, the corresponding lines and points within the coplanar spaces are accurately reprojected onto the original multi-view images. Ultimately, aerial triangulation is conducted using cross-platform corresponding features to achieve refined building modeling. Experimental results demonstrate that our method surpasses existing techniques in reconstructing more detailed 3D models of buildings. Notably, the accuracy of cross-platform image matching exceeds 99.27%, highlighting the effectiveness and robustness of our proposed method.