Camera Calibration: A Quick and Easy Way to Determine the Scale Factor
IEEE Transactions on Pattern Analysis and Machine Intelligence
Camera Calibration with Distortion Models and Accuracy Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling and calibration of automated zoom lenses
Modeling and calibration of automated zoom lenses
Tracking based structure and motion recovery for augmented video productions
VRST '01 Proceedings of the ACM symposium on Virtual reality software and technology
Implicit and Explicit Camera Calibration: Theory and Experiments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lens distortion calibration using point correspondences
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
On the Epipolar Geometry Between Two Images with Lens Distortion
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
Drift Detection and Removal for Sequential Structure from Motion Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Visualization and Computer Graphics
Camera Models and Fundamental Concepts Used in Geometric Computer Vision
Foundations and Trends® in Computer Graphics and Vision
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Lens distortions in off-the-shelf or wide-angle cameras block the road to high accuracy Structure and Motion Recovery (SMR) from video sequences. Neglecting lens distortions introduces a systematic error buildup which causes recovered structure and motion to bend and inhibits turntable or other loop sequences to close perfectly. Locking back onto previously reconstructed structure can become impossible due to the large drift caused by the error buildup. Bundle adjustments are widely used to perform an ultimate post-minimization of the total reprojection error. However, the initial recovered structure and motion needs to be close to optimal to avoid local minima. We found that bundle adjustments cannot remedy the error buildup caused by ignoring lens distortions. The classical approach to distortion removal involves a preliminary distortion estimation using a calibration pattern, known geometric properties of perspective projections or only 2D feature correspondences. Often the distortion is assumed constant during camera usage and removed from the images before applying SMR algorithms. However, lens distortions can change by zooming, focusing and temperature variations. Moreover, when only the video sequence is available preliminary calibration is often not an option. This paper addresses all fore-mentioned problems by sequentially recovering lens distortions together with structure and motion from video sequences without tedious pre-calibrations and allowing lens distortions to change over time. The devised algorithms are fairly simple as they only use linear least squares techniques. The unprocessed video sequence forms the only input and no severe restrictions are placed on viewed scene geometry. Therefore, the accurate recovery of structure and motion is fully automated and widely applicable. The experiments demonstrate the necessity of modeling lens distortions to achieve high accuracy in recovered structure and motion.