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19 September 2018 Towards euclidean auto-calibration of stereo camera arrays
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Multi-camera networks are becoming ubiquitous in a variety of applications related to medical imaging, education, entertainment, autonomous vehicles, civil security, defense etc. The foremost task in deploying a multi-camera network is camera calibration, which usually involves introducing an object with known geometry into the scene. However, most of the aforementioned applications necessitate non-intrusive automatic camera calibration. To this end, a class of camera auto-calibration methods imposes constraints on the camera network rather than on the scene. In particular, the inclusion of stereo cameras in a multi-camera network is known to improve calibration accuracy and preserve scale. Yet most of the methods relying on stereo cameras use custom-made stereo pairs, and such stereo pairs can definitely be considered imperfect; while the baseline distance can be fixed, one cannot guarantee the optical axes of two cameras to be parallel in such cases. In this paper, we propose a characterization of the imperfections in those stereo pairs with the assumption that such imperfections are within a considerably small, reasonable deviation range from the ideal values. Once the imperfections are quantified, we use an auto-calibration method to calibrate a set of stereo cameras. We provide a comparison of these results with those obtained under parallel optical axes assumption. The paper also reports results obtained from the utilization of synthetic visual data.
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Roopak R. Tamboli, Kiran K. Vupparaboina, M. Shanmukh Reddy, Peter A. Kara, Aron Cserkaszky, Maria G. Martini, Ashutosh Richhariya, and Soumya Jana "Towards euclidean auto-calibration of stereo camera arrays", Proc. SPIE 10747, Optical System Alignment, Tolerancing, and Verification XII, 107470I (19 September 2018);

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