Abstract
This paper describes a new algorithm for disparity estimation using trinocular stereo. The three cameras are placed in a right angled configuration. A graph is then constructed whose nodes represent the individual pixels and whose edges are along the epipolar lines. Using the well known uniqueness and ordering constraint for pair by pair matches simultaneously, a path with the least matching cost is found using dynamic programming and the disparity filled along the path. This process is repeated iteratively until the disparity at all the pixels are filled up. To demonstrate the effectiveness of our approach, we present results from real world images and compare it with the traditional line by line stereo using dynamic programming.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Agrawal, M. and Davis, L.S. 2001. A probabilistic framework for surface reconstruction from multiple images. In Proceedings IEEE Conference on Computer Vision and Pattern Recognition, Lihue, Hawaii, Dec. 2001.
Agrawal, M., Harwood, D., Duraiswami, R., Davis, L., and Luther, P. 2000. Three dimensional ultrastructure from transmission electron. microscope tilt series. In Proceedings Indian Conference on Vision Graphics and Image Processing, Bangalore, India, Dec. 2000.
Birchfield, S. and Tomasi, C. 1998. Depth discontinuities by pixel-to-pixel stereo. In Proceedings Sixth IEEE International Conference on Computer Vision, Mumbai, India, Jan. 1998, pp. 1073–1080.
Birchfield, S. and Tomasi, C. 1999. Multiway cut for stereo and motion with slanted surfaces. In Proceedings of the Seventh International Conference on Computer Vision, Sept. 1999, pp. 489–495.
Boykov, Y., Veksler, O., and Zabih, R. 1998. Markov random fields with efficient approximations. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 648–655.
Boykov, Y., Veksler, O., and Zabih, R. 1999. Fast approximate energy minimization via graph cuts. In Proc. International Conference on Computer Vision, pp. 377–384.
Cox, I., Hingorani, S., Maggs, B., and Rao, S. 1996. A maximum likelihood stereo algorithm. Computer Vision and Image Understanding, 63(3):542–567.
Hartley, R. and Zisserman, A. 2000. Multiple ViewGeometry in Computer Vision. Cambridge Uinversity Press: Cambridge.
Ishikawa, H. and Geiger, D. 1998. Occlusions, discontinuities, and epipolar lines in stereo. In Fifth European Conference on Computer Vision, LNCS 1406, Springer Verlag, June 1998, pp. 232–248.
Kanade, T. and Okutomi, M. 1994. Astereo matching algorithm with an adaptive window: Theory and experiment. PAMI, 16(9):920–932.
Ohta, Y. and Kanade, T. 1985. Stereo by intra and inter-scanline search using dynamic programming. IEEE Transactions on Pattern Analysis and Machine Intelligence, 7(2):139–154.
Ohta, Y., Watanabe, M., and Ikeda, K. 1986. Improving depth map by right-angled trinocular stereo. In ICPR86, pp. 519–521.
Pritchett, P. and Zisserman, A. 1998. Wide baseline stereo matching. In Proc. 6th Int'l Conf. on Computer Vision, pp. 754–760.
Roy, S. and Cox, I. 1998. A maximum-flow formulation of the n-camera stereo correspondence problem. In Proc. 6th Intl. Conference on Computer Vision, pp. 492–499.
Stewart, C.V. and Dyer, C.R. 1988. The trinocular general support algorithm: Athree-camera stereo algorithm for overcoming binocular matching errors. In Proc 2nd Int. Conf. on Computer Vision, pp. 134–138.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Agrawal, M., Davis, L.S. Trinocular Stereo Using Shortest Paths and the Ordering Constraint. International Journal of Computer Vision 47, 43–50 (2002). https://doi.org/10.1023/A:1017478504047
Issue Date:
DOI: https://doi.org/10.1023/A:1017478504047