A new approach to the maximum flow problem
STOC '86 Proceedings of the eighteenth annual ACM symposium on Theory of computing
Visual reconstruction
Experimental study of minimum cut algorithms
SODA '97 Proceedings of the eighth annual ACM-SIAM symposium on Discrete algorithms
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Belief Propagation for Early Vision
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Free-viewpoint depth image based rendering
Journal of Visual Communication and Image Representation
High-accuracy stereo depth maps using structured light
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Depth Estimation from Three Cameras Using Belief Propagation: 3D Modelling of Sumo Wrestling
CVMP '11 Proceedings of the 2011 Conference for Visual Media Production
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Dense depth-map extraction approaches either suffer from limited accuracy and robustness when run on natural images or from long computation times due to complex global optimizations. Recent improvements in massively parallel execution architectures found in today's graphics processing units motivated us to parallelize the global optimization process. In this article we present our analytical approach and a parallel implementation in OpenCL of a multi-label graph-cut algorithm. Our approach accomodates a third camera perspective through which we can improve occlusion handling in order to generate high quality depth maps. Through experiments on natural images we show that our implementation scales linearly and achieves close-to realtime performance.