Abstract
This paper describes a new method for matching line segments between two images in order to compute the relative camera pose. This approach improves the camera pose for images lacking stable point features but where straight line segments are available. The line matching algorithm is divided into two stages: At first, scale-invariant feature points along the lines are matched incorporating a one-dimensional homography. Then, corresponding line segments are selected based on the quality of the estimated homography and epipolar constraints. Based on two line segment correspondences the relative orientation between two images can be calculated.
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
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 1615–1630 (2005)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)
Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: British Machine Vision Conference, pp. 384–393 (2002)
Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: Exploring photo collections in 3d. In: SIGGRAPH Conference Proceedings, pp. 835–846 (2006)
Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24, 381–395 (1981)
Nistér, D.: An efficient solution to the five-point relative pose problem. IEEE Pattern Analysis and Machine Intelligence 26, 756–770 (2004)
Schmid, C., Zisserman, A.: Automatic line matching across views. In: Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition, pp. 666–672 (1997)
Bay, H., Ferrari, V., Van Gool, L.: Wide-baseline stereo matching with line segments. In: Proceedings of the 2005 Conference on Computer Vision and Pattern Recognition, pp. 329–336 (2005)
Meltzer, J., Soatto, S.: Edge descriptors for robust wide-baseline correspondence. In: Proceedings of the 2008 Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)
Briggs, A.J., Detweiler, C., Li, Y., Mullen, P.C., Scharstein, D.: Matching scale-space features in 1d panoramas. Computer Vision and Image Understanding 103, 184–195 (2006)
Xie, J., Beigi, M.S.: A scale-invariant local descriptor for event recognition in 1d sensor signals. In: Proceedings of the 2009 IEEE International Conference on Multimedia and Expo, Piscataway, NJ, USA, pp. 1226–1229. IEEE Press, Los Alamitos (2009)
Thormählen, T., Broszio, H., Wassermann, I.: Robust line-based calibration of lens distortion from a single view. In: Proceedings of MIRAGE 2003, pp. 105–112 (2003)
Brown, M., Lowe, D.: Invariant features from interest point groups. In: British Machine Vision Conference, pp. 656–665 (2002)
Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004) ISBN: 0521540518
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Reisner-Kollmann, I., Reichinger, A., Purgathofer, W. (2010). 3D Camera Pose Estimation Using Line Correspondences and 1D Homographies. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6454. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17274-8_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-17274-8_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17273-1
Online ISBN: 978-3-642-17274-8
eBook Packages: Computer ScienceComputer Science (R0)