Abstract: We present an approach to two problems in 3- D object recognition from a single 2-D image: the problem of recognizing an unexpected object from a large database and the problem of searching the image for a particular object (expected object recognition). Most work in 3-D object recognition has focused on the latter problem, with few expected object recognition systems able to scale to larger databases. Avoiding the large indexing ambiguity requires the use of more discriminating image primitives than are typically employed. In previous work, we describe the representation and recovery of high-level indexing structures composed of volumetric primitives. In this paper, we describe a recognition strategy that, integrated with our shape recovery strategy, supports the recognition of both unexpected and expected objects. Unexpected object recognition is formulated as a matching of recovered 3-D interpretations of the image to objects models, while expected object recognition uses knowledge of the target object to constrain both the matching and shape recovery processes. !27
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