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.
Highlights
With the development of laser scanning and image matching technology, three-dimensional (3D)information has increasingly attracted researchers’ attention
This paper proposes a multi-view dense point cloud generation algorithm based on low-altitude remote sensing images
The method integrates the advantages of Multi-View Stereo and epipolar-based dense matching methods and generates a denser point cloud with more details
Summary
With the development of laser scanning and image matching technology, three-dimensional (3D)information has increasingly attracted researchers’ attention. Images can be accepted from any type of camera [14], including calibrated or uncalibrated images, images taken from smartphones or tablets [15], images captured from digital cameras or frames intercepted from video streams [16]; It is low in cost; Point cloud data contains color information; and Theoretically, it may produce much denser point clouds [17]. Low-altitude remote sensing images have been considered a popular data source for large-scale 3D modeling [18]. In addition to sub-decimeter high-resolution imagery [19], a low-altitude remote sensing platform has several advantages including: flexibility, low cost, simplicity of operation, and ease of maintenance [20]. This paper proposes a multi-view dense point cloud generation algorithm based on low-altitude remote sensing images. The proposed method exploited Patch-based Multi View Stereo (PMVS) [21]
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