Abstract
The recently published KITTI stereo dataset provides a new quality of stereo imagery with partial ground truth for benchmarking stereo matchers. Our aim is to test the value of stereo confidence measures (e.g. a left-right consistency check of disparity maps, or an analysis of the slope of a local interpolation of the cost function at the taken minimum) when applied to recorded datasets, such as published with KITTI. We choose popular measures as available in the stereo-analysis literature, and discuss a naive combination of these. Evaluations are carried out using a sparsification strategy. While the best single confidence measure proved to be the right-left consistency check for high disparity map densities, the best overall performance is achieved with the proposed naive measure combination. We argue that there is still demand for more challenging datasets and more comprehensive ground truth.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision 47, 7–42 (2002)
The University of Auckland: .enpeda.. image sequence analysis test site (EISATS), http://www.mi.auckland.ac.nz/EISATS
Heidelberg Collaboratory for Image Processing: Robust Vision Challenge, http://hci.iwr.uni-heidelberg.de/Static/challenge2012/
Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: Computer Vision and Pattern Recognition (CVPR), Providence, USA (2012)
Banks, J., Corke, P.I.: Quantitative evaluation of matching methods and validity measures for stereo vision. I. J. Robotic Res. 20, 512–532 (2001)
Hu, X., Mordohai, P.: Evaluation of stereo confidence indoors and outdoors. In: [19], pp. 1466–1473
Wedel, A., Brox, T., Vaudrey, T., Rabe, C., Franke, U., Cremers, D.: Stereoscopic scene flow computation for 3D motion understanding. International Journal of Computer Vision 95, 29–51 (2011)
Shimizu, M., Okutomi, M.: Precise sub-pixel estimation on area-based matching. In: ICCV, pp. 90–97 (2001)
Merrell, P., Akbarzadeh, A., Wang, L., Mordohai, P., Frahm, J.-M., Yang, R., Nistér, D., Pollefeys, M.: Real-time visibility-based fusion of depth maps. In: ICCV, pp. 1–8 (2007)
Gehrig, S.K., Scharwächter, T.: A real-time multi-cue framework for determining optical flow confidence. In: ICCV Workshops, pp. 1978–1985 (2011)
Aodha, O.M., Brostow, G.J., Pollefeys, M.: Segmenting video into classes of algorithm-suitability. In: [19], pp. 1054–1061
Humayun, A., Aodha, O.M., Brostow, G.J.: Learning to find occlusion regions. In: CVPR, pp. 2161–2168. IEEE (2011)
Hirschmüller, H.: Stereo processing by semiglobal matching and mutual information. IEEE Trans. Pattern Anal. Mach. Intell. 30, 328–341 (2008)
Egnal, G.: Mutual information as a stereo correspondence measure. Computer and Information Science, University of Pennsylvania, Philadelphia, USA, Tech. Rep. MS-CIS-00-20 (2000)
Hirschmüller, H., Scharstein, D.: Evaluation of cost functions for stereo matching. In: CVPR (2007)
Scharstein, D., Szeliski, R.: Stereo matching with nonlinear diffusion. International Journal of Computer Vision 28, 155–174 (1998)
Shi, J., Tomasi, C.: Good features to track. In: CVPR, pp. 593–600. IEEE (1994)
Okutomi, M., Katayama, Y., Oka, S.: A simple stereo algorithm to recover precise object boundaries and smooth surfaces. International Journal of Computer Vision 47, 261–273 (2002)
The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2010, San Francisco, CA, USA, June 13-18. IEEE (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Haeusler, R., Klette, R. (2012). Analysis of KITTI Data for Stereo Analysis with Stereo Confidence Measures. In: Fusiello, A., Murino, V., Cucchiara, R. (eds) Computer Vision – ECCV 2012. Workshops and Demonstrations. ECCV 2012. Lecture Notes in Computer Science, vol 7584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33868-7_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-33868-7_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33867-0
Online ISBN: 978-3-642-33868-7
eBook Packages: Computer ScienceComputer Science (R0)