Abstract
We present a novel variational method for estimating dense disparity maps from stereo images. It integrates the epipolar constraint into the currently most accurate optic flow method (Brox et al. 2004). In this way, a new approach is obtained that offers several advantages compared to existing variational methods: (i) It preservers discontinuities very well due to the use of the total variation as solution-driven regulariser. (ii) It performs favourably under noise since it uses a robust function to penalise deviations from the data constraints. (iii) Its minimisation via a coarse-to-fine strategy can be theoretically justified. Experiments with both synthetic and real-world data show the excellent performance and the noise robustness of our approach.
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References
Alvarez, L., Deriche, R., Sánchez, J., Weickert, J.: Dense disparity map estimation respecting image derivatives: a PDE and scale-space based approach. Journal of Visual Communication and Image Representation 13(1/2), 3–21 (2002)
Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: High accuracy optic flow estimation based on a theory for warping. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 25–36. Springer, Heidelberg (2004)
Faugeras, O.: Three-Dimensional Computer Vision: A Geometric Viewpoint. MIT Press, Cambridge (1993)
Felsberg, M.: Disparity from monogenic phase. In: Van Gool, L. (ed.) DAGM 2002. LNCS, vol. 2449, pp. 248–256. Springer, Heidelberg (2002)
Fröhlinghaus, T., Buhmann, J.M.: Regularizing phase-based stereo. In: Proc. 13th International Conference on Pattern Recognition, Vienna, Austria, August 1996, pp. 451–455 (1996)
Grimson, W.E.L.: Computational experiments with a feature based stereo algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence 7, 17–34 (1985)
Hansen, M., Daniilidis, K., Sommer, G.: Optimization of stereo disparity estimation using the instantaneous frequency. In: Sommer, G., Daniilidis, K., Pauli, J. (eds.) CAIP 1997. LNCS, vol. 1296, pp. 321–328. Springer, Heidelberg (1997)
Hirschmüller, H., Innocent, P., Garibaldi, J.: Real-time correlation-based stereo vision with reduced border errors. International Journal of Computer Vision 47(1-3), 229–246 (2002)
Kim, H., Sohn, K.: Hierarchical disparity estimation with energy based regularization. In: Proc. Tenth IEEE International Conference on Image Processing, Barcelona, Spain, September 2003, vol. 1, pp. 373–376 (2003)
Mansouri, A.-R., Mitchie, A., Konrad, J.: Selective image diffusion: application to disparity estimation. In: Proc. 1998 IEEE International Conference on Image Processing, Chicago, IL, vol. 3, pp. 114–118. IEEE Computer Society Press, Los Alamitos (1998)
Marr, D., Poggio, T.: Cooperative computation of stereo disparity. Science 194, 283–287 (1976)
Robert, L., Deriche, R.: Dense depth map reconstruction: A minimization and regularization approach which preserves discontinuities. In: Buxton, B.F., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1064, pp. 439–451. Springer, Heidelberg (1996)
Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60, 259–268 (1992)
Scharstein, D., Szeliski, R.: Stereo matching with non-linear diffusion. In: Proc. 1996 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 343–350. IEEE Computer Society Press, San Francisco (1996)
Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision 47(1-3), 7–42 (2002)
Wötzel, J., Koch, R.: Multi-camera real-time depth estimation with discontinuity on PC graphics hardware. In: Proc. 17th International Conference on Pattern Recognition, Cambridge, United Kingdom, vol. 1, pp. 741–744 (2004)
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Slesareva, N., Bruhn, A., Weickert, J. (2005). Optic Flow Goes Stereo: A Variational Method for Estimating Discontinuity-Preserving Dense Disparity Maps. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds) Pattern Recognition. DAGM 2005. Lecture Notes in Computer Science, vol 3663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550518_5
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DOI: https://doi.org/10.1007/11550518_5
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