Vision-Based 3D Object Localization Using Probabilistic Models of Appearance

The ability to accurately localize objects in an observed scene is regarded as an important precondition for many practical applications including automatic manufacturing, quality assurance, or human-robot interaction. A popular method to recognize three-dimensional objects in two-dimensional images is to apply so-called view-based approaches. In this paper, we present an approach that uses a probabilistic view-based object recognition technique for 3D localization of rigid objects. Our system generates a set of views for each object to learn an object model which is applied to identify the 6D pose of the object in the scene. In practical experiments carried out with real image data as well as rendered images, we demonstrate that our approach is robust against changing lighting conditions and high amounts of clutter.