We present a fully automatic approach for reconstructing compact 3D building models from large-scale airborne point clouds. A major challenge of urban reconstruction from airborne LiDAR point clouds lies in that the vertical walls are typically missing. Based on the observation that urban buildings typically consist of planar roofs connected with vertical walls to the ground, we propose an approach to infer the vertical walls directly from the data. With the planar segments of both roofs and walls, we hypothesize the faces of the building surface, and the final model is obtained by using an extended hypothesis-and-selection-based polygonal surface reconstruction framework. Specifically, we introduce a new energy term to encourage roof preferences and two additional hard constraints into the optimization step to ensure correct topology and enhance detail recovery. Experiments on various large-scale airborne LiDAR point clouds have demonstrated that the method is superior to the state-of-the-art methods in terms of reconstruction accuracy and robustness. In addition, we have generated a new dataset with our method consisting of the point clouds and 3D models of 20k real-world buildings. We believe this dataset can stimulate research in urban reconstruction from airborne LiDAR point clouds and the use of 3D city models in urban applications.

翻译：我们提出了一种全自动方法，用于从大规模机载点云中重建紧凑的三维建筑模型。城市环境从机载LiDAR点云重建的主要挑战在于垂直墙面通常缺失。基于城市建筑通常由与地面连接的垂直墙面和平面屋顶组成的观察，我们提出了一种直接从数据中推断垂直墙面的方法。利用屋顶和墙面的平面分割结果，我们假设建筑表面的面片，并通过扩展的基于假设与选择的多边形表面重建框架获得最终模型。具体而言，我们在优化步骤中引入了一个新的能量项以鼓励屋顶偏好，并添加两个额外的硬约束以确保正确拓扑并增强细节恢复。在多种大规模机载LiDAR点云上的实验表明，该方法在重建精度和鲁棒性方面优于现有最先进方法。此外，我们利用该方法生成了一个包含2万栋真实建筑的点云和三维模型的新数据集。我们相信该数据集将促进基于机载LiDAR点云的城市重建研究以及三维城市模型在城市应用中的使用。