Abstract
This paper describes a probabilistic Laplacian surface propagation (PLSP) framework for a robust stereo matching under severe radiometric variations. We discover that a progressive scheme overcomes an inherent limitation for this task, while most conventional efforts have been focusing on designing a robust cost function. We propose the ground control surfaces (GCSs) designed as progressive unit, which alleviates the problems of conventional progressive methods and superpixel based methods, simultaneously. Moreover, we introduce a novel confidence measure for stereo pairs taken under radiometric variations based on the probability of correspondences. Specifically, the PLSP estimates the GCSs from initial sparse disparity maps using a weighted least-square. The GCSs are then propagated on a superpixel graph with a surface confidence weighting. Experimental results show that the PLSP outperforms state-of-the-art robust cost function based methods and other propagation methods for the stereo matching under radiometric variations.
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Bumsub Ham—WILLOW project-team, Département d’Informatique de l’Ecole Normale Supérieure, ENS/Inria/CNRS UMR 8548.
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Notes
- 1.
In order to evaluate the robustness of only surface propagation, the LSP only expands the propagation unit as a superpixel without the confidence weighting for GCSs.
References
Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. IJCV 47, 7–42 (2002)
Hirschmüller, H., Scharstein, D.: Evaluation of stereo matching costs on images with radiometric differences. TPAMI 31, 1582–1599 (2009)
Zabih, R., Woodfill, J.: Non-parametric local transforms for computing visual correspondence. In: Eklundh, J.-O. (ed.) ECCV 1994. LNCS, pp. 151–158. Springer, Heidelberg (1994)
Heo, Y., Lee, K., Lee, S.: Robust stereo matching using adaptive normalized cross-correlation. TPAMI 33, 807–822 (2011)
Kim, S., Ham, B., Kim, B., Sohn, K.: Mahalanobis distance cross-correlation for illumination invariant stereo matching. TCSVT 24, 1844–1859 (2014)
Kim, J., Kolmogorov, V., Zabih, R.: Visual correspondence using energy minimization and mutual information. In: ICCV (2003)
Heo, Y., Lee, K., Lee, S.: Mutual information-based stereo matching combined with sift descriptor in log-chromaticity color space. In: CVPR (2009)
Szeliski, R., Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Tappen, M., Rother, C.: A comparative study of energy minimization methods for markov random fields. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 16–29. Springer, Heidelberg (2006)
Levin, A., Lischinski, D., Weiss, Y.: Colorization using optimization. In: SIGGRAPH (2006)
Krishnan, D., Fattal, R., Szeliski, R.: Efficient preconditioning of laplacian matrices for computer graphics. In: SIGGRAPH (2013)
Wang, L., Yang, R.: Global stereo matching leveraged by sparse ground control points. In: CVPR (2011)
Hawe, S., Kleinsteuber, M., Diepold, K.: Dense disparity maps from sparse disparity measurements. In: ICCV (2011)
Sun, X., Mei, X., Zhou, M., Wang, H.: Stereo matching with reliable disparity propagation. In: 3DIMPVT (2011)
Yamaguchi, K., Hazan, T., McAllester, D., Urtasun, R.: Continuous Markov random fields for robust stereo estimation. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part V. LNCS, vol. 7576, pp. 45–58. Springer, Heidelberg (2012)
Lu, J., Yang, H., Min, D., Do, M.: Patchmatch filter: Efficient edge-aware filtering meets randomized search for fast correspondence field estimation. In: CVPR (2013)
Bleyer, M., Rother, C., Kohli, P.: Surface stereo with soft segmentation. In: CVPR (2010)
Hong, L., Chen, G.: Segment-based stereo matching using graph cuts. In: CVPR (2004)
Klaus, A., Sormann, M., Karner, K.: Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure. In: ICPR (2006)
Sinha, S., Steedly, D., Szeliski, R.: Piecewise planar stereo for image-based rendering. In: ICCV (2009)
Wang, Z., Zheng, Z.: A region based stereo matching algorithm using cooperative optimization. In: CVPR (2008)
Hwang, Y., Lee, J., Kweon, I., Kim, S.: Color transfer using probabilistic moving least squares. In: CVPR (2014)
Lowe, D.: Distinctive image features from scale-invariant keypoints. IJCV 60, 91–110 (2004)
Chia, A., Zhuo, S., Gupta, R., Tai, Y., Cho, S., Tan, P., Lin, S.: Semantic colorization with internet images. In: SIGGRAPH (2011)
Lang, M., Wang, O., Aydic, T., Smolic, A., Gross, M.: Practical temporal consistency for image-based graphics applications. In: SIGGRAPH (2012)
Ma, Z., He, K., Wei, Y., Sun, J., Wu, E.: Constant time weighted median filtering for stereo matching and beyond. In: ICCV (2013)
He, K., Sun, J., Tang, X.: Guided image filtering. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 1–14. Springer, Heidelberg (2010)
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. TPAMI 34, 2274–2282 (2012)
Acknowledgement
This research was supported by the MSIP(Ministry of Science, ICT and Future Planning), Korea, under the ITRC(Information Technology Research Center) support program (NIPA-2014-H0301-14-1012) supervised by the NIPA(National IT Industry Promotion Agency).
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Kim, S., Ham, B., Ryu, S., Kim, S.J., Sohn, K. (2015). Robust Stereo Matching Using Probabilistic Laplacian Surface Propagation. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision – ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9003. Springer, Cham. https://doi.org/10.1007/978-3-319-16865-4_24
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