A novel data-driven based high-precision building roof contour full-automatic extraction and structured 3D reconstruction method combining stereo images and LiDAR points

The structured-3D-reconstruction of buildings is essential for rapid urban-3D-modeling. The existing data-driven methods for full-automatic building-contour-extraction fail to meet mapping-accuracy-requirements and lack robustness. Therefore, this paper proposes a novel data-driven algorithm for roof-contour-extraction and structured-3D-reconstruction by fusing stereo-images and LiDAR-point-clouds. Firstly, an improved Sobel-based-operator is designed, which effectively improves the detection rate of building-edge-features. Secondly, based on a buffer constraint strategy, a method for automatically removing non-building linear features that away from the building boundaries is proposed. Subsequently, to tackle the challenge of distinguishing pseudo-building linear features, an equidistant mapping method based on LiDAR profile and image features is presented, yielding high-confidence building-edge-features. Finally, to address the fragmentation and incompleteness issues on low-level image features, a set of rules for refining building-boundary-features and achieving complete contour closure is further proposed. Based on these, structured-3D-models are constructed quickly. Additionally, a vector-based evaluation metric RMEC is proposed, which allows for a better assessment of intelligent mapping. Experiments conducted on the ISPRS benchmark datasets indicate that the average correctness (CR) of the proposed method for extracting building-roof-contours is 96.3%, which is better than the other three methods; the average RMSE of the proposed method has a 12.3% improvement than the comparison method.