Abstract
In a previous paper we described a system which recursively recovers a super-resolved three dimensional surface model from a set of images of the surface. In that paper we assumed that the camera calibration for each image was known. In this paper we solve two problems. Firstly, if an estimate of the surface is already known, the problem is to calibrate a new image relative to the existing surface model. Secondly, if no surface estimate is available, the relative camera calibration between the images in the set must be estimated. This will allow an initial surface model to be estimated. Results of both types of estimation are given.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Smelyanskiy, V.N., Cheeseman, P., Maluf, D.A. and Morris, R.D.: Bayesian Super-Resolved Surface Reconstruction from Images. Proceedings of the International Conference on Computer Vision and Pattern Recognition, Hilton Head, 2000
Harris, C.: A Combined Corner and Edge Detector. Proceedings of the Alvey Vision Conference, pp 189–192, 1987
Fischler, M.A. and Bolles, R.C.: Random Sample Concensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communications of the ACM, June 1981, vol. 24, no. 6, pp 381–395
Zhang, Z., Deriche, R., Faugeras, O. and Luong, Q.T.: A Robust Technique for Matching Two Uncalibrated Images Through the Recovery of the Unknown Epipolar Geometry. AI Journal, vol. 78, pp 87–119, 1994
Rousseeuw, P.J.: Robust Regression and Outlier Detection. Wiley, New York, 1987
Torr, P.H.S. and Davidson, C.: IMPSAC: Synthesis of Importance Sampling and Random Sample Consensus. Technical Report, Microsoft Research, Cambridge, UK
Torr, P.H.S. and Zisserman, A.: MLESAC: A New Robust Estimator with Application to Estimating Image Geometry. Computer Vision and Image Understanding, vol 1, pp 138–156, 2000
Hartley, R.I.: In Defense of the Eight Point Algorithm IEEE Transactions on Pattern Aalysis and Machine Intelligence, vol 19, pp 580–594, June 1997
Longuet-Higgins, H.C.: A Computer Algorithm for Reconstructing a Scene from Two Projections. Nature, vol 293, pp 133–135, 1981
Faugeras, O.: Three-Dimensional Computer Vision MIT Press, 1993
More, J.: The Levenberg-Marquardt Algorithm, Implementation and Theory. In G. A. Watson, editor, Numerical Analysis, Lecture Notes in Mathematics 630. Springer-Verlag, 1977.
Dellaert, F., Seitz, S.M., Thorpe, C.E., and Thrun, S.: Structure from Motion Without Correspondences. Proceedings of the International Conference on Computer Vision and Pattern Recognition, Hilton Head, 2000
Zhang, Z.: A Flexible New Technique for Camera Calibration. Technical Report MSR-TR-98-71, Microsoft Research, Redmond, Washington
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Morris, R.D., Smelyanskiy, V.N., Cheeseman, P.C. (2001). Matching Images to Models — Camera Calibration for 3-D Surface Reconstruction. In: Figueiredo, M., Zerubia, J., Jain, A.K. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2001. Lecture Notes in Computer Science, vol 2134. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44745-8_8
Download citation
DOI: https://doi.org/10.1007/3-540-44745-8_8
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42523-6
Online ISBN: 978-3-540-44745-0
eBook Packages: Springer Book Archive