Sven J. Dickinson, Alex Pentland, Azriel Rosenfeld

Abstract

Abstract We present an approach to the recovery and recognition of 3-D objects from a single 2-D image. The approach is motivated by the need for more powerful indexing primitives, and shifts the burden of recognition from the model-based verification of simple image features to the bottom-up recovery of complex volumetric primitives. Given a recognition domain consisting of a database of objects, we first select a set of object-centered 3-D volumetric modeling primitives that can be used to construct the objects. Next, using a CAD system, we generate the set of aspects of the primitives. Unlike typical aspect-based recognition systems that use aspects to model entire objects, we use aspects to model the parts from which the objects are constructed. Consequently, the number of aspects is fixed and independent of the size of the object database. To accommodate the matching of partial aspects due to primitive occlusion, we introduce a hierarchical aspect representation based on the projected surfaces of the primitives; a set of conditional probabilities captures the ambiguity of mappings between the levels of the hierarchy. From a region segmentation of the input image, we present a novel formulation of the primitive 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 relations. Subgraphs of the resulting graph, in which nodes represent 3-D primitives and arcs represent primitive connections, are used as indices to the object database. The verification of object hypotheses consists of a topological verification of the recovered graph, rather than a geometrical verification of image features. A system has been built to demonstrate the approach, and it has been successfully applied to both synthetic and real imagery.