We propose an approach for learning visual models of object categories in an unsupervised manner in which we first build a large-scale complex network which captures the interacti...
This paper addresses the problem of exactly inferring the maximum a posteriori solutions of discrete multi-label MRFs or CRFs with higher order cliques. We present a framework to ...
We demonstrate how to exploit reflections for accurate registration of shiny objects: The lighting environment can be retrieved from the reflections under a distant illumination a...
Pascal Lagger, Mathieu Salzmann, Vincent Lepetit, ...
The required amount of labeled training data for object detection and classification is a major drawback of current methods. Combining labeled and unlabeled data via semisupervise...
We present a framework for computing optimal transformations, aligning one point set to another, in the presence of outliers. Example applications include shape matching and regis...