Abstract
Matching points are the direct data sources of the fundamental matrix, camera parameters, and point cloud calculation. Thus, their uncertainty has a direct influence on the quality of image-based 3D reconstruction and is dependent on the number, accuracy, and distribution of the matching points. This study mainly focuses on the uncertainty of matching point distribution. First, horizontal dilution of precision (HDOP) is used to quantify the feature point distribution in the overlapping region of multiple images. Then, the quantization method is constructed. H D O P ∗ ¯ , the average of 2 × arctan ( H D O P × n 5 − 1 ) / π on all images, is utilized to measure the uncertainty of matching point distribution on 3D reconstruction. Finally, simulated and real scene experiments were performed to describe and verify the rationality of the proposed method. We found that the relationship between H D O P ∗ ¯ and the matching point distribution in this study was consistent with that between matching point distribution and 3D reconstruction. Consequently, it may be a feasible method to predict the quality of 3D reconstruction by calculating the uncertainty of matching point distribution.
Highlights
A considerable amount of research has been conducted on image-based three-dimensional (3D)reconstruction in traditional 3D terrain reconstruction [1], urban reconstruction [2], and vegetation reconstruction [3]
The matching point distribution deviating from the center point of the overlapping regions affected the accuracy of 3D points
In a series of simulated scene experiments, we found that the relationship between the matching points’ distribution and horizontal dilution of precision (HDOP)∗ were consistent with that between the matching point distribution and the accuracy of 3D points
Summary
A considerable amount of research has been conducted on image-based three-dimensional (3D)reconstruction in traditional 3D terrain reconstruction [1], urban (rigid object) reconstruction [2], and vegetation (nonrigid object) reconstruction [3]. A considerable amount of research has been conducted on image-based three-dimensional (3D). The rapid extraction and construction of 3D models from images has an important role in spatial data acquisition. Carrivick [4] summarized the quantitative research on errors in 3D reconstruction, which has mainly focused on the data source, results, and other similar aspects. Image-based 3D reconstruction is a complex process that involves several steps: Feature extraction and matching, fundamental matrix computation, camera calibration, and point cloud reconstruction. A large number of matching points are extracted from stereo pairs for the calculation of the fundamental matrix, camera parameters, and point clouds. Matching points are a direct data source for the other steps of image-based 3D reconstruction, and their uncertainty has an important influence on the quality of 3D reconstruction
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