Disambiguating Visual Relations Using Loop Constraints

Repetitive and ambiguous visual structures in general pose a severe problem in many computer vision applications. Identification of incorrect geometric relations between images solely based on low level features is not always possible, and a more global reasoning approach about the consistency of the estimated relations is required. We propose to utilize the typically observed redundancy in the hypothesized relations for such reasoning, and focus on the graph structure induced by those relations. Chaining the (reversible) transformations over cycles in this graph allows to build suitable statistics for identifying inconsistent loops in the graph. This data provides indirect evidence for conflicting visual relations. Inferring the set of likely false positive geometric relations from these non-local observations is formulated in a Bayesian framework. We demonstrate the utility of the proposed method in several applications, most prominently the computation of structure and motion from images.