Abstract
The 3D reconstruction technique using the straight-line segments as features has high precision and low computational cost. The method is especially suitable for large-scale urban datasets. However, the line matching step in the existing method has a mismatching problem. The two main reasons for this problem are the line detection result is not located at the true edge of the image and there is no consistency check of the matching pair. In order to solve this problem, a linear correction and matching method for 3D reconstruction of target line structure is proposed in this paper. Firstly, the edge features of the image are extracted to obtain a binarized edge map. Then, the extended gradient map is calculated using the edge map and the gradient to establish the gradient gravitational map. Secondly, the straight-line detection method is used to extract all the linear features used for the 3D reconstruction image, and the linear position is corrected by the gradient gravitational map. Finally, the point feature matching result is used to calculate the polar line, and the line matching results of the adjacent three images are used to determine the final partial check feature area. Then, random sampling is used to obtain the feature similarity check line matching result in the small neighborhood. The aforementioned steps can eliminate the mismatched lines. The experimental results demonstrate that the 3D model obtained using the proposed method has higher integrity and accuracy than the existing methods.
Highlights
Using the camera imaging model to recover the 3D structure of the object from the acquired 2D image sequence is one of the classic problems in the field of computer vision. e 3D reconstruction refers to the establishment of mathematical models suitable for computer representation and processing of 3D objects. e 3D reconstruction is the basis for processing, manipulating, and analyzing the properties of 3D objects in a computer environment
Because the feature point dataset is very large, the multiview stereo (MVS) algorithm has a slow processing speed, which often takes a large amount of time and computing memory
The image-based 3D reconstruction technology is affected by factors such as lighting and occlusion when extracting feature points
Summary
Using the camera imaging model to recover the 3D structure of the object from the acquired 2D image sequence is one of the classic problems in the field of computer vision. e 3D reconstruction refers to the establishment of mathematical models suitable for computer representation and processing of 3D objects. e 3D reconstruction is the basis for processing, manipulating, and analyzing the properties of 3D objects in a computer environment. By analyzing the pinhole camera model, epipolar geometry, and various line segment detection algorithms, it is found that 3D reconstruction based on line matching is feasible. Bay et al [13] used line segments from two uncalibrated images to determine the relative camera poses and to compute a piecewise planar 3D model This method is not suitable for processing more than two images, is not robust when dealing with unstable lighting conditions, and is unable to handle some outdoor scenes. In 2014, Micusik and Wildenauer [16] proposed a SLAM-like system with line matching through narrow baselines and showed impressive results, especially for indoor scenes This method only attempts to estimate the camera pose estimation and 3D reconstruction through line segments is extremely difficult. An accurate and complete 3D reconstruction result is obtained
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