This paper discusses the reconstruction of LoD2 building models from 2D and
3D data for large-scale urban environments. Traditional methods involve the use
of LiDAR point clouds, but due to high costs and long intervals associated with
acquiring such data for rapidly developing areas, researchers have started
exploring the use of point clouds generated from (oblique) aerial images.
However, using such point clouds for traditional plane detection-based methods
can result in significant errors and introduce noise into the reconstructed
building models. To address this, this paper presents a method for extracting
rooflines from true orthophotos using line detection for the reconstruction of
building models at the LoD2 level. The approach is able to extract relatively
complete rooflines without the need for pre-labeled training data or
pre-trained models. These lines can directly be used in the LoD2 building model
reconstruction process. The method is superior to existing plane
detection-based methods and state-of-the-art deep learning methods in terms of
the accuracy and completeness of the reconstructed building. Our source code is
available at this https URL

该论文讨论了如何从 2D 和 3D 数据中重建 LoD2 建筑模型，并针对大规模城市环境进行了研究。通过提取真实正交照片中的屋顶线条，无需预先标记的训练数据或预先训练模型，该方法能够提取相对完整的屋顶线条，并直接用于建筑模型的重建过程，从而在重建建筑物的准确性和完整性方面优于现有方法和深度学习方法。