Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robot Vision
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
An Experimental Comparison of Min-cut/Max-flow Algorithms for Energy Minimization in Vision
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Multi-camera Scene Reconstruction via Graph Cuts
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Markov Random Fields with Efficient Approximations
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A Task-Specific Contour Tracker for Ultrasound
MMBIA '00 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis
Recovery and Tracking of Continuous 3D Surfaces from Stereo Data Using a Deformable Dual-Mesh
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Energy Minimization via Graph Cuts: Settling What is Possible
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Exact optimization for Markov random fields with convex priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This paper presents an optimization framework for the 3D reconstruction of the surfaces from stereo image pairs. The method is based on employing popular graph cut methods under the dual mesh optimization technique. The constructed system produces noticeably better results by running two separate optimization processes that communicate with each other. The communication mechanism makes our system more robust against local minima and it produces extra side information about the scene such as the unreliable image sections. We validated our system by running experiments on real data with ground truth and we compared our results with the other optimization methods, which showed the accuracy and effectiveness of our method.