3-D object recognition is a difficult and yet an important problem in computer vision. A 3-D object recognition system has two major components, object modeling (or representation) and matching of stored representations to those derived from the sensed image. In this paper, we focus on the topic of model-building for 3-D objects. Most existing 3-D object recognition systems construct models either manually, or by training (learning models from multiple images of an object). Both these approaches have not been very satisfactory, particularly in designing object recognition systems which can handle a large number of objects. Recent interest in integrating mechanical CAD systems and vision systems has led to a third type of model-building for vision: adaptation of preexisting CAD models of objects for recognition. If a solid model of an object to be recognized is already available in a manufacturing database, then we should be able to infer automatically a model appropriate for vision tasks from the manufacturing model. We have developed such a system which uses 3-D object descriptions created on a commercial CAD system and expressed in both the industry-standard IGES form and a polyhedral approximation, and performs geometric inferencing to obtain a relational graph representation of the object which can be stored in a database of models for object recognition. Relational graph models contain both view-independent information extracted from the IGES description, and view-dependent information (patch areas) extracted from synthetic views of the object. We believe that a system like ours is needed to efficiently create a large database (more than 100 objects) of 3-D models to evaluate matching strategies.
