Global stereo reconstruction under second order smoothness priors

Second-order priors on the smoothness of 3D surfaces are a better model of typical scenes than first-order priors. However, stereo reconstruction using global inference algorithms, such as graph-cuts, has not been able to incorporate second-order priors because the triple cliques needed to express them yield intractable (non-submodular) optimization problems. This paper shows that inference with triple cliques can be effectively optimized. Our optimization strategy is a development of recent extensions to α-expansion, based on the “QPBO” algorithm [5, 14, 26]. The strategy is to repeatedly merge proposal depth maps using a novel extension of QPBO. Proposal depth maps can come from any source, for example fronto-parallel planes as in α-expansion, or indeed any existing stereo algorithm, with arbitrary parameter settings. Experimental results demonstrate the usefulness of the second-order prior and the efficacy of our optimization framework. An implementation of our stereo frame...

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Oliver J. Woodford, Philip H. S. Torr, Ian D. Reid, Andrew W. Fitzgibbon
• Keywords: Computer Vision, CVPR 2008, Proposal Depth Maps, Second-order Prior, Triple Cliques