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Remote Sens. 2016, 8(1), 5; doi:10.3390/rs8010005

Investigation on the Weighted RANSAC Approaches for Building Roof Plane Segmentation from LiDAR Point Clouds

1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China
2
Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA
3
National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China
*
Author to whom correspondence should be addressed.
Academic Editors: Devrim Akca, Zhong Lu and Prasad Thenkabail
Received: 23 September 2015 / Revised: 9 December 2015 / Accepted: 15 December 2015 / Published: 23 December 2015
View Full-Text   |   Download PDF [9945 KB, uploaded 23 December 2015]   |  

Abstract

RANdom SAmple Consensus (RANSAC) is a widely adopted method for LiDAR point cloud segmentation because of its robustness to noise and outliers. However, RANSAC has a tendency to generate false segments consisting of points from several nearly coplanar surfaces. To address this problem, we formulate the weighted RANSAC approach for the purpose of point cloud segmentation. In our proposed solution, the hard threshold voting function which considers both the point-plane distance and the normal vector consistency is transformed into a soft threshold voting function based on two weight functions. To improve weighted RANSAC’s ability to distinguish planes, we designed the weight functions according to the difference in the error distribution between the proper and improper plane hypotheses, based on which an outlier suppression ratio was also defined. Using the ratio, a thorough comparison was conducted between these different weight functions to determine the best performing function. The selected weight function was then compared to the existing weighted RANSAC methods, the original RANSAC, and a representative region growing (RG) method. Experiments with two airborne LiDAR datasets of varying densities show that the various weighted methods can improve the segmentation quality differently, but the dedicated designed weight functions can significantly improve the segmentation accuracy and the topology correctness. Moreover, its robustness is much better when compared to the RG method. View Full-Text
Keywords: 3D point clouds; building reconstruction; building roof segmentation; weighted RANSAC 3D point clouds; building reconstruction; building roof segmentation; weighted RANSAC
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MDPI and ACS Style

Xu, B.; Jiang, W.; Shan, J.; Zhang, J.; Li, L. Investigation on the Weighted RANSAC Approaches for Building Roof Plane Segmentation from LiDAR Point Clouds. Remote Sens. 2016, 8, 5.

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