From volumes to views: an approach to 3-D object recognition

The authors present an approach to the recovery and recognition of 3-D objects from a single 2-D image. Given a recognition domain consisting of a database of objects, they select a set of object-centered 3-D volumetric modeling primitives that can be used to construct the objects. They take the set of primitives and generate a hierarchical aspect representation based on their projected surfaces; conditional probabilities capture the ambiguity of mappings between levels of the hierarchy. From a region segmentation of the input image, they present a novel formulation of the recovery problem based on grouping the regions into aspects. Once the aspects are recovered, they use the aspect hierarchy to infer a set of volumetric primitives and their connectivity. The recovered primitives are then used as indices into the object database for recognition