Urban modeling from LiDAR point clouds is an important topic in computer vision, computer graphics, photogrammetry and remote sensing. 3D city models have found a wide range of applications in smart cities, autonomous navigation, urban planning and mapping etc. However, existing datasets for 3D modeling mainly focus on common objects such as furniture or cars. Lack of building datasets has become a major obstacle for applying deep learning technology to specific domains such as urban modeling. In this paper, we present a urban-scale dataset consisting of more than 160 thousands buildings along with corresponding point clouds, mesh and wire-frame models, covering 16 cities in Estonia about 998 Km2. We extensively evaluate performance of state-of-the-art algorithms including handcrafted and deep feature based methods. Experimental results indicate that Building3D has challenges of high intra-class variance, data imbalance and large-scale noises. The Building3D is the first and largest urban-scale building modeling benchmark, allowing a comparison of supervised and self-supervised learning methods. We believe that our Building3D will facilitate future research on urban modeling, aerial path planning, mesh simplification, and semantic/part segmentation etc.

这篇论文介绍了一个城市建模的数据集Building3D，它包括超过160,000幢建筑物的点云、网格和线框模型，覆盖爱沙尼亚的16个城市，面积约为998平方公里。作者通过对先进算法的评估发现Building3D存在高内类别差异、数据不平衡和大规模噪声等挑战。这个数据集是首个最大规模的城市建模基准，可以用于监督学习和自监督学习方法的比较。作者认为Building3D将促进未来在城市建模、航路规划、网格简化和语义/部分分割等领域的研究。

Building3D: 从点云中学习屋顶结构的城市级数据集和基准