Primitive-based shape modeling and recognition

We 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, we select a set of object-centered 3-D volumetric modeling primitives that can be used to construct the objects. Next, we 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, we present a novel formulation of the recovery problem based on grouping the regions into aspects. No domain dependent heuristics are used; we exploit only the probabilities inherent in the aspect hierarchy. Once the aspects are recovered, we use the aspect hierarchy to infer a set of volumetric primitives and their connectivity. Subgraphs of the resulting graph, in which nodes represent 3-D primitives and arcs represent primitive connections, are used as indices into the object database. Object verification consists of a topological verification of the recovered graph rather than a geometrical verification of image features.