Abstract
Abstract. Recently, a camera self-calibration algorithm was reported which solves for pose, focal length and radial distortion using a minimal set of four 2D-to-3D point correspondences. In this paper, we present an empirical analysis of the algorithm’s accuracy using highfidelity point correspondences. In particular, we use images of circular markers arranged in a regular planar grid, obtain the centroids of the marker images, and pass those as input point correspondences to the algorithm. We compare the resulting reprojection errors against those obtained from a benchmark calibration based on the same data. Our experiments show that for low-noise point images the self-calibration technique performs at least as good as the benchmark with a simplified distortion model.
Highlights
Estimating the position and orientation of a camera along with its intrinsic properties is a fundamental problem in computer vision
Applying the algebraic minimal problem solver by (Bujnak et al, 2011) and the associated proposed self-calibration method for images of only a few, but confirmed, co-planar points arranged in a grid pattern, and verifying its feasibility for this type of task up to a specific qualitative accuracy
We presented a comparison of a specific camera selfcalibration method that includes modeling of radial distortion to a benchmark calibration method to assess the former’s relative performance in terms of reprojection error for images of a regular grid of points
Summary
Estimating the position and orientation of a camera along with its intrinsic properties is a fundamental problem in computer vision. Self-calibration methods have become popular (Hartley, 1997, Triggs, 1998, Li and Hartley, 2005) Those techniques do not use a calibration object, but rather exploit structural features of a static scene viewed from different positions. We already provide 3D point coordinates both to the calibration technique and to the self-calibration method In this scenario, the traditional calibration method serves as a benchmark or “gold standard”. The traditional calibration method serves as a benchmark or “gold standard” We consider this to be justified, as the method by (Zhang, 2000) is implemented as the calibration method of choice in some form or another in a variety of (open-source) software packages, including such popular software as OpenCV (Bradski and Kaehler, 2008) and the MATLAB Camera Calibration Toolbox by (Bouguet, 2004).
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