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The matching function for the problem of stereo reconstruction or optical flow has been traditionally designed as a function of the distance between the features describing matched pixels.
Non-parametric local transforms for computing visual correspondence
Zabih, R., Woodfill, J.: · 1994
Earlier work this paper cites.
Additive Logistic Regression: a Statistical View of Boosting
Friedman, J., Hastie, T., Tibshirani, R.: · 2000
Earlier work this paper cites.
The opencv library (2000)
Bradski, G.: · 2000
Earlier work this paper cites.
Contour and texture analysis for image segmentation
Malik, J., Belongie, S., Leung, T., Shi, J.: · 2001
Earlier work this paper cites.
Computing visual correspondence with occlusions via graph cuts
Kolmogorov, V., Zabih, R.: · 2001
Earlier work this paper cites.
A taxonomy and evaluation of dense two-frame stereo correspondence algorithms
Scharstein, D., Szeliski, R.: · 2002
Earlier work this paper cites.
Distinctive image features from scale-invariant keypoints
Lowe, D.G.: · 2004
Earlier work this paper cites.
TextonBoost
Shotton, J., Winn, J., Rother, C., Criminisi, A.: · 2006
Earlier work this paper cites.
Evaluation of cost functions for stereo matching
Hirschmuller, H., Scharstein, D.: · 2007
Earlier work this paper cites.
Matching local self-similarities across images and videos
Shechtman, E., Irani, M.: · 2007
Earlier work this paper cites.
Semantic texton forests for image categorization and segmentation
Shotton, J., Johnson, M., Cipolla, R.: · 2008
Cited alongside, same era.
Kernel codebooks for scene categorization
Gemert, J.C.V., Geusebroek, J., Veenman, C.J., Smeulders, A.W.M.: · 2008
Cited alongside, same era.
Associative hierarchical CRFs for object class image segmentation
Ladicky, L., Russell, C., Kohli, P., Torr, P.H.S.: · 2009
Cited alongside, same era.
Efficient large-scale stereo matching
Geiger, A., Roser, M., Urtasun, R.: · 2010
Cited alongside, same era.
Efficient large-scale stereo matching
Geiger, A., Roser, M., Urtasun, R.: · 2010
Cited alongside, same era.
Large displacement optical flow: descriptor matching in variational motion estimation
Brox, T., Malik, J.: · 2011
Cited alongside, same era.
Visual recognition using local quantized patterns
Hussain, S.u., Triggs, B.: · 2012
Later among the works it cites.
Robust monocular epipolar flow estimation
Yamaguchi, K., McAllester, D., Urtasun, R.: · 2012
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Iterative semi-global matching for robust driver assistance systems
Hermann, S., Klette, R.: · 2012
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Deepflow: Large displacement optical flow with deep matching
Weinzaepfel, P., Revaud, J., Harchaoui, Z., Schmid, C.: · 2013
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City-scale change detection in cadastral 3d models using images
Taneja, A., Ballan, L., Pollefeys, M.: · 2013
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Robust monocular epipolar flow estimation
Yamaguchi, K., McAllester, D., Urtasun, R.: · 2013
Later among the works it cites.
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Real-time human pose recognition in parts from single depth images
Shotton, J., Fitzgibbon, A., Cook, M., Blake, A.: · 2011
Cited alongside, same era.
Efficient inference in fully connected CRFs with gaussian edge potentials
Krahenbuhl, P., Koltun, V.: · 2011
Cited alongside, same era.
Are we ready for autonomous driving? the kitti vision benchmark suite
Geiger, A., Lenz, P., Urtasun, R.: · 2012
Cited alongside, same era.
A naturalistic open source movie for optical flow evaluation
Butler, D.J., Wulff, J., Stanley, G.B., Black, M.J.: · 2012
Cited alongside, same era.
Weighted semi-global matching and center-symmetric census transform for robust driver assistance
Spangenberg, R., Langner, T., Rojas, R.: · 2013
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Minimizing tgv-based variational models with non-convex data terms
Ranftl, R., Pock, T., Bischof, H.: · 2013
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Fast and accurate large-scale stereo reconstruction using variational methods
Kuschk, G., Cremers, D.: · 2013
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Computing the stereo matching cost with a convolutional neural network
Zbontar, J., LeCun, Y.: · 2014
Later among the works it cites.