A Multi-View Dense Point Cloud Generation Algorithm Based on Low-Altitude Remote Sensing Images
1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2
Collaborative Innovation Center for Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Academic Editors: Diego Gonzalez-Aguilera, Fabio Remondino, Nicolas Baghdadi and Prasad S. Thenkabail
Remote Sens. 2016, 8(5), 381; https://doi.org/10.3390/rs8050381
Received: 27 January 2016 / Revised: 22 April 2016 / Accepted: 27 April 2016 / Published: 4 May 2016
(This article belongs to the Special Issue Remote Sensed Data and Processing Methodologies for 3D Virtual Reconstruction and Visualization of Complex Architectures)
This paper presents a novel multi-view dense point cloud generation algorithm based on low-altitude remote sensing images. The proposed method was designed to be especially effective in enhancing the density of point clouds generated by Multi-View Stereo (MVS) algorithms. To overcome the limitations of MVS and dense matching algorithms, an expanded patch was set up for each point in the point cloud. Then, a patch-based Multiphoto Geometrically Constrained Matching (MPGC) was employed to optimize points on the patch based on least square adjustment, the space geometry relationship, and epipolar line constraint. The major advantages of this approach are twofold: (1) compared with the MVS method, the proposed algorithm can achieve denser three-dimensional (3D) point cloud data; and (2) compared with the epipolar-based dense matching method, the proposed method utilizes redundant measurements to weaken the influence of occlusion and noise on matching results. Comparison studies and experimental results have validated the accuracy of the proposed algorithm in low-altitude remote sensing image dense point cloud generation.
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Keywords:
multi-view stereo; dense point cloud; image matching
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MDPI and ACS Style
Shao, Z.; Yang, N.; Xiao, X.; Zhang, L.; Peng, Z. A Multi-View Dense Point Cloud Generation Algorithm Based on Low-Altitude Remote Sensing Images. Remote Sens. 2016, 8, 381. https://doi.org/10.3390/rs8050381
AMA Style
Shao Z, Yang N, Xiao X, Zhang L, Peng Z. A Multi-View Dense Point Cloud Generation Algorithm Based on Low-Altitude Remote Sensing Images. Remote Sensing. 2016; 8(5):381. https://doi.org/10.3390/rs8050381
Chicago/Turabian StyleShao, Zhenfeng; Yang, Nan; Xiao, Xiongwu; Zhang, Lei; Peng, Zhe. 2016. "A Multi-View Dense Point Cloud Generation Algorithm Based on Low-Altitude Remote Sensing Images" Remote Sens. 8, no. 5: 381. https://doi.org/10.3390/rs8050381
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