Abstract
A 3D spherical panoramic epipolar line based on essential matrix is proposed in this study to solve the search range to improve calculation efficiency and reliability of the matching. First, this method uses the essential matrix of computer vision principles to establish the epipolar geometry model of spherical panoramic image. Second, it derives the epipolar line mathematical equations and characteristics of spherical panoramas. Third, it calculates statistics on the distribution law of epipolar images. Experimental results show that the feasibility of the method proposed in this study. The proposed method effectively verified the trajectory of the spherical panoramic epipolar line, finished distribution of epipolar line sequence trajectory and decreased the search range of the corresponding points. The findings help lay a solid foundation for the matching and measurement of the panoramic measurement model and the generation of depth maps.
Highlights
The epipolar constraint can reduce the image matching search space from two dimensions to one, thereby improving computational efficiency and reliability
The analysis provides a theoretical basis for the application of epipolar lines in spherical panoramic images
Computer vision and photogrammetry theory are used in establishing the relationship between two projection planes and their corresponding points to realize the 3D position of a projection point
Summary
The epipolar constraint can reduce the image matching search space from two dimensions to one, thereby improving computational efficiency and reliability. Current research work and applications of epipolar lines are mainly based on pinhole central projection with a limited field of view (FOV), resulting in various problems such as image blind spots and insufficient overlaps. A spherical stereovision system based on the double fisheye lens has been proposed This system can expand the FOV to 190° and expand the epipolar lines into parallel ones, to which the search range of points with the same name is limited. This study carries out an in-depth analysis of the projection geometry of spherical panoramic images, uses the essential matrix of computer vision principles to establish the epipolar geometry model of such image, derives its mathematical equations and characteristics, and calculates statistics on the distribution law of epipolar images. Comparison shows that the proposed method is more versatile than the photogrammetric method, which requires certain initial conditions to derive the geometric relationship of the epipolar line
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