The reconstruction of buildings using inhomogeneous and unstructured point clouds is a challenging task for photogrammetry and computer vision research communities. A new approach for 3D building surface modeling, based on closed constraints, is proposed. First, a region growth algorithm is applied to fit the input point clouds by a set of candidate planes. Then, additional candidate planes are generated from the initial planes according to a rigid transformation followed by expanding the original primitive set to the candidate model set through generation rules. Furthermore, an energy function is employed to combine the data fitting errors with the structural constraints at the model selection stage. Finally, the 3D building surface model is generated from the candidate set through energy minimization. More precisely speaking, we adopt the surface optimization scheme that enforces the 3D polygonal surfaces of the building to be consistent with a priori geometric structures. Our approach was assessed using multi-source datasets with different densities, noise levels covering diverse and complex structures. The experimental results demonstrated that the proposed approach achieves better accuracy and robustness than those of several state-of-the-art methods.

对于摄影测量学和计算机视觉研究社区而言，使用不均匀且非结构化的点云来重建建筑物是一项艰巨的任务。提出了一种基于封闭约束的3D建筑表面建模的新方法。首先，应用区域增长算法以通过一组候选平面拟合输入点云。然后，根据刚性变换从初始平面生成其他候选平面，然后通过生成规则将原始图元集扩展为候选模型集。此外，在模型选择阶段，采用能量函数将数据拟合误差与结构约束相结合。最后，通过能量最小化从候选集生成3D建筑表面模型。更准确地说，我们采用表面优化方案，该方案将建筑物的3D多边形表面强制与先验几何结构保持一致。我们的方法是使用具有不同密度的多源数据集进行评估的，噪声水平涵盖了各种复杂的结构。实验结果表明，与几种最新方法相比，该方法具有更高的准确性和鲁棒性。