Camera calibration based on arbitrary parallelograms |
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| Affiliation: | 1. Robotics Institute, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, USA;2. Department of EECS, Korea Advanced Institute of Science and Technology, Kusong-Dong 373-1, Yuseong-Gu, Daejeon, Republic of Korea;1. State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China;2. Department of Prosthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China;1. National Clinical Research Center for Infectious Disease, Shenzhen Third People’s Hospital, Department of Hepatology, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen 518112, Guangdong, China;2. Fujian Provincial Reproductive Medicine Center, Fujian Maternity and Children’s Hospital Affiliated to Fujian Medical University, Fuzhou 350001, Fujian, China;3. Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Hepatopancreatobiliary Surgery I, Peking University Cancer Hospital & Institute, Beijing 100142, China;1. School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, PR China;2. TAMS Group, Department of Informatics, University of Hamburg, Germany |
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| Abstract: | Existing algorithms for camera calibration and metric reconstruction are not appropriate for image sets containing geometrically transformed images for which we cannot apply the camera constraints such as square or zero-skewed pixels. In this paper, we propose a framework to use scene constraints in the form of camera constraints. Our approach is based on image warping using images of parallelograms. We show that the warped image using parallelograms constrains the camera both intrinsically and extrinsically. Image warping converts the calibration problems of transformed images into the calibration problem with highly constrained cameras. In addition, it is possible to determine affine projection matrices from the images without explicit projective reconstruction. We introduce camera motion constraints of the warped image and a new parameterization of an infinite homography using the warping matrix. Combining the calibration and the affine reconstruction results in the fully metric reconstruction of scenes with geometrically transformed images. The feasibility of the proposed algorithm is tested with synthetic and real data. Finally, examples of metric reconstructions are shown from the geometrically transformed images obtained from the Internet. |
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