SingleRecon: Reconstructing Building 3-D Models of LoD1 From a Single Off-Nadir Remote Sensing Image

3-D building models are one of the most intuitive and widely used forms for understanding urban buildings. Generating 3-D building models based on a single off-nadir satellite image is an economical and rapid method, particularly valuable in large-scale 3-D reconstruction scenarios with limited time. In this article, we propose a novel pipeline for automatically reconstructing level of detail 1 (LoD1) 3-D building models based on a single off-nadir satellite remote sensing image. Our pipeline is built upon a multitask neural network called off-nadir building reconstruction network (ONBuildingNet), which extracts building roof polygons and offsets from the image. Using this information, the pipeline computes the building footprint polygons and heights, constructs LoD1 building models, and then extract textures from the off-nadir image. ONBuildingNet introduces our proposed cross-field auxiliary task and multiscale mask head to extract building roof polygons with accurate shapes. We have demonstrated through extensive experiments that our pipeline can automatically and rapidly construct LoD1 3-D urban building models. In addition, our proposed ONBuildingNet outperforms current state-of-the-art methods in extracting more shape accurate building roof polygons, thereby enhancing the accuracy of the final 3-D models produced by our pipeline. Experimental results demonstrate that our method for reconstructing 3-D models of urban building scenes has strong visualization effects, with an average height error of 3.3 m.