Bayesian Structure from Motion

:We formulate structure from motion as a Bayesian inference problem, and use a Markov chain Monte Carlo sampler to sample the posterior on this problem. This results in a method that can identify both small and large tracker errors, and yields reconstructions that are stable in the presence of these errors. Furthermore, the method gives detailed informationon the range of ambiguities in structure given a particular dataset, and requires no special geometric formulation to cope with degenerate situations. Motion segmentation is obtained by a layer of discrete variables associating a point with an object. We demonstrate a sampler that successfully samples an approximation to the marginal on this domain, producing a relatively unambiguous segmentation.

• Type: Conference
• Year: 1999
• Where: ICCV
• Authors: David A. Forsyth, Sergey Ioffe, John A. Haddon
• Keywords: Bayesian Inference Problem, Computer Vision, ICCV 1999, Large Tracker Errors, Markov Chain Monte Carlo