CAD-based computer vision: from CAD models to relational graphs

The topic of model-building for 3-D objects is examined. Most 3-D object recognition systems construct models either manually or by training. Neither approach has 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 it should be possible to infer automatically a model appropriate for vision tasks from the manufacturing model. Such a system has been developed. It 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. It is argued that such a system is needed to efficiently create a large database (more than 100 objects) of 3-D models to evaluate matching strategies.<>