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。