The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
International Journal of Computer Vision
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Estimation and Segmentation of Dense Motion Fields
International Journal of Computer Vision
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Model-Based Motion Estimation
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Occlusions, Discontinuities, and Epipolar Lines in Stereo
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Multi-camera Scene Reconstruction via Graph Cuts
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
A Space-Sweep Approach to True Multi-Image Matching
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Markov Random Fields with Efficient Approximations
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
A Maximum-Flow Formulation of the N-Camera Stereo Correspondence Problem
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extracting View-Dependent Depth Maps from a Collection of Images
International Journal of Computer Vision - Special Issue on Research at Microsoft Corporation
Symmetric Stereo Matching for Occlusion Handling
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Segment-Based Stereo Matching Using Belief Propagation and a Self-Adapting Dissimilarity Measure
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Stereo for Image-Based Rendering using Image Over-Segmentation
International Journal of Computer Vision
Local stereo matching with adaptive support-weight, rank transform and disparity calibration
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast adaptive graph-cuts based stereo matching
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
Multi-label Depth Estimation for Graph Cuts Stereo Problems
Journal of Mathematical Imaging and Vision
High-accuracy stereo depth maps using structured light
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Exact optimization for Markov random fields with convex priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-label Depth Estimation for Graph Cuts Stereo Problems
Journal of Mathematical Imaging and Vision
SCoBeP: Dense image registration using sparse coding and belief propagation
Journal of Visual Communication and Image Representation
A homography transform based higher-order MRF model for stereo matching
Pattern Recognition Letters
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We describe here a method to compute the depth of a scene from a set of at least two images taken at known view-points. Our approach is based on an energy formulation of the 3D reconstruction problem which we minimize using a graph-cut approach that computes a local minimum whose energy is comparable (modulo a multiple constant) with the energy of the absolute minimum. As usually done, we treat the input images symmetrically, match pixels using photoconsistency, treat occlusion and visibility problems and consider a spatial regularization term which preserves discontinuities. The details of the graph construction as well as the proof of the correctness of the method are given. Moreover we introduce a multi-label refinement algorithm in order to increase the number of depth labels without significantly increasing the computational complexity. Finally we compared our algorithm with the results available in the Middlebury database.