Recent work in qualitative shape recovery and object recognition has focused on solving the what is it" problem, while avoiding the where is it" problem. In contrast, typical CAD-based recognition systems have focused on the where is it" problem, while assuming they know what the object is. Although each approach addresses an important aspect of the 3-D object recognition problem, each falls short in addressing the complete problem of recognizing and localizing 3-D objects from a large database. In this paper, we rst synthesize a new approach to shape recovery for 3-D object recognition that decouples recognition from localization by combining basic elements from these two approaches. Specically, we use qualitative shape recovery and recognition techniques to provide strong tting constraints on physics-based deformable model recovery techniques. Secondly, we extend our previously developed technique of fitting deformable models to occluding image contours to the case of image data captured under general orthographic, perspective, and stereo projections. On one hand, integrating qualitative knowledge of the object being fitted to the data, along with knowledge of occlusion supports a much more robust and accurate quantitative fitting. On the other hand, recovering object pose and quantitative surface shape not only provides a richer description for indexing, but supports interaction with the world when object manipulation is required. This paper presents the approach in detail and applies it to real imagery.