Learning 3D recognition models for general objects from unlabeled imagery: an experiment in intelligent brute force

In this paper we explorer the problem of training a general, 3D abject recognition system from unlabeled imagery. In particular, we attempt to identify critical issues and stumbling blocks associated with minimizing the supervision necessary to train such a system. As class learning seems to be a relatively slow and resource intensive process even for people, we consider approaches and perform experiments that entail on the order of 10/sup 1/5 basic operations, even for relatively small databases. This is the current practical limit of the computation that can be achieved. For experiments, we use a recognition system developed previously.