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Remote Sens. 2016, 8(9), 710; doi:10.3390/rs8090710

Edge Detection and Feature Line Tracing in 3D-Point Clouds by Analyzing Geometric Properties of Neighborhoods

1
School of Resource and Environmental Sciences, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China
2
Chinese Academy of Surveying and Mapping, No. 28 Lianhuachixi Road, Beijing 100830, China
*
Author to whom correspondence should be addressed.
Academic Editors: Jie Shan, Juha Hyyppä, Gonzalo Pajares Martinsanz and Prasad S. Thenkabail
Received: 10 March 2016 / Revised: 23 August 2016 / Accepted: 24 August 2016 / Published: 1 September 2016
(This article belongs to the Special Issue Airborne Laser Scanning)
View Full-Text   |   Download PDF [9232 KB, uploaded 1 September 2016]   |  

Abstract

This paper presents an automated and effective method for detecting 3D edges and tracing feature lines from 3D-point clouds. This method is named Analysis of Geometric Properties of Neighborhoods (AGPN), and it includes two main steps: edge detection and feature line tracing. In the edge detection step, AGPN analyzes geometric properties of each query point’s neighborhood, and then combines RANdom SAmple Consensus (RANSAC) and angular gap metric to detect edges. In the feature line tracing step, feature lines are traced by a hybrid method based on region growing and model fitting in the detected edges. Our approach is experimentally validated on complex man-made objects and large-scale urban scenes with millions of points. Comparative studies with state-of-the-art methods demonstrate that our method obtains a promising, reliable, and high performance in detecting edges and tracing feature lines in 3D-point clouds. Moreover, AGPN is insensitive to the point density of the input data. View Full-Text
Keywords: 3D edge; Edge detection; Feature line tracing; RANdom SAmple Consensus (RANSAC); Angular gap 3D edge; Edge detection; Feature line tracing; RANdom SAmple Consensus (RANSAC); Angular gap
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Ni, H.; Lin, X.; Ning, X.; Zhang, J. Edge Detection and Feature Line Tracing in 3D-Point Clouds by Analyzing Geometric Properties of Neighborhoods. Remote Sens. 2016, 8, 710.

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