A Multi-View Dense Point Cloud Generation Algorithm Based on Low-Altitude Remote Sensing Images

Shao, Zhenfeng; Yang, Nan; Xiao, Xiongwu; Zhang, Lei; Peng, Zhe

doi:10.3390/rs8050381

Open AccessArticle

A Multi-View Dense Point Cloud Generation Algorithm Based on Low-Altitude Remote Sensing Images

by

Zhenfeng Shao

^1,2,†,

Nan Yang

^1,2,*,†

,

Xiongwu Xiao

^1,2,†

,

Lei Zhang

^1,2 and

Zhe Peng

^1,2

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.

Remote Sens. 2016, 8(5), 381; https://doi.org/10.3390/rs8050381

Submission 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)

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Abstract

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.

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 Style

Shao, Zhenfeng, Nan Yang, Xiongwu Xiao, Lei Zhang, and Zhe Peng. 2016. "A Multi-View Dense Point Cloud Generation Algorithm Based on Low-Altitude Remote Sensing Images" Remote Sensing 8, no. 5: 381. https://doi.org/10.3390/rs8050381

APA Style

Shao, Z., Yang, N., Xiao, X., Zhang, L., & Peng, Z. (2016). A Multi-View Dense Point Cloud Generation Algorithm Based on Low-Altitude Remote Sensing Images. Remote Sensing, 8(5), 381. https://doi.org/10.3390/rs8050381

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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A Multi-View Dense Point Cloud Generation Algorithm Based on Low-Altitude Remote Sensing Images

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