ABSTRACT

Accurate roof segmentation is one of the key steps for automatically constructing three-dimensional (3-D) building models. Building roofs can differ significantly in terms of their size, shape complexity, and number, rendering many existing airborne Light Detection And Ranging (LiDAR) roof segmentation methods ineffective. Thus, the applicability and precision of these methods need to be improved. For this purpose, this paper proposes a new roof segmentation method for airborne LiDAR point clouds. The proposed method integrates a novel region growing strategy and RANdom SAmple Consensus (RANSAC), applying these approaches to extract several reliable roof patches and then performing an iterative process to merge roof patches based on their parameters and the concept of inlier selection of RANSAC. Finally, unsegmented points and segmentation results are refined by voting in a local neighbourhood. The experimental results show that the proposed method can effectively segment the roofs of buildings with different complexity and sizes. As the basic evaluation primitive, the average segmentation correctness was found to be 95.67 and 97.85% when using a roof and a single point, respectively, which can provide reliable information for applications, such as 3-D building model reconstruction.

摘要

准确的屋顶分割是自动构建三维（3-D）建筑模型的关键步骤之一。建筑屋顶在尺寸，形状复杂度和数量方面可能存在很大差异，从而使许多现有的机载光检测和测距（LiDAR）屋顶分割方法无效。因此，需要提高这些方法的适用性和准确性。为此，本文提出了一种新的机载LiDAR点云车顶分割方法。所提出的方法集成了一种新颖的区域增长策略和RANdom SAmple Consensus（RANSAC），应用这些方法提取了几个可靠的屋顶斑块，然后基于其参数和RANSAC的内部选择概念执行了迭代过程以合并屋顶斑块。最后，未分段的点和分段结果通过在本地社区中投票进行完善。实验结果表明，该方法可以有效地分割复杂度和大小不同的建筑物屋顶。作为基本评估原语，使用屋顶和单点时的平均分割正确性分别为95.67和97.85％，这可以为诸如3-D建筑模型重建之类的应用提供可靠的信息。