Accurate reconstruction of roofs with dormers is challenging. Without careful separation of the dormer points from the points on the roof surface, the estimation of the roof areas is distorted. The characteristic distortion of the density distribution in comparison to the expected normal distribution is the starting point of our method. We propose a hierarchical method which improves roof reconstruction from LiDAR point clouds in a model‐based manner, separating dormer points from roof points using classification methods. The key idea is to exploit probability density functions to reveal roof properties and to skilfully design the features for a supervised learning method using support vector machines. The approach is tested based on real data as well as simulated point clouds.

中文翻译：

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寻找大海捞针：基于概率密度的3D点云分类和重建屋顶窗

用天窗准确地改造屋顶是一项挑战。如果不小心将屋顶窗点与屋顶表面上的点分开，屋顶面积的估计就会失真。与预期的正态分布相比，密度分布的特征失真是我们方法的出发点。我们提出了一种分层方法，该方法以基于模型的方式改进了LiDAR点云的屋顶重建，使用分类方法将屋顶窗点与屋顶点分离。关键思想是利用概率密度函数来揭示屋顶特性，并使用支持向量机巧妙地设计监督学习方法的特征。该方法基于真实数据以及模拟点云进行了测试。