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3D reconstruction of buildings from LiDAR data considering various types of roof structures

机译:考虑各种类型屋顶结构的LiDAR数据对建筑物的3D重建

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摘要

In this article, a different approach has been proposed to detect and reconstruct buildings in 3D space. In this regard, some potentially primary features were produced and a genetic algorithm (GA) was employed in order to select the optimum features. The selected features were utilized to detect the building by using k-nearest neighbour (k-NN) algorithm. The detection results were used as inputs of reconstruction procedure. The proposed approach for 3D reconstruction consists of three main steps: roof planes were separated in the first step. Then, the corners of each plane boundary were extracted in order to provide the roof reconstruction possibility. Finally, the walls were reconstructed and merged to roofs and the final 3D model was obtained. Results evaluation indicated that the average value of buildings detection in the test areas was 87.84% in Quality. Moreover, the average value of buildings reconstruction in the test areas was 76.95% in object based Quality, and 95.66% in Quality of building planes that were greater than 25 m(2), respectively. Also, the average of altimetric and planimetric RMS of the test areas were 0.3 m and 0.75 m, respectively.
机译:在本文中,提出了一种不同的方法来检测和重建3D空间中的建筑物。在这方面,产生了一些潜在的主要特征,并采用遗传算法(GA)来选择最佳特征。通过使用k最近邻(k-NN)算法,利用选定的特征来检测建筑物。检测结果用作重建程序的输入。提议的3D重建方法包括三个主要步骤:在第一步中分离屋顶平面。然后,提取每个平面边界的角,以提供屋顶重建的可能性。最后,将墙壁重建并合并到屋顶,并获得最终的3D模型。结果评估表明,测试区域中建筑物检测的平均值为质量的87.84%。此外,测试区域中建筑物重建的平均值分别是基于对象的质量为76.95%和大于25 m(2)的建筑物平面的质量为95.66%。同样,测试区域的平均测高和平面RMS分别为0.3 m和0.75 m。

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