Recognizing three-dimensional objects by comparing two-dimensional images

In this paper we address the problem of recognizing an object from a novel viewpoint, given a single "model" view of that object. As is common in model-based recognition, objects and images are represented as sets of feature points. We present an efficient algorithm for determining whether two sets of image points (in the plane) could be projections of a common object (a three-dimensional point set). The method relies on the fact that two sets of points in the plane are orthographic projections of the same three-dimensional point set exactly when they have a common projection onto a line. This is a form of the well-known epipolar constraint used in stereopsis. Our algorithm can be used to recognize an object by comparing a stored two-dimensional view of the object against an unknown view, without requiring the correspondence between points in the views to be known a priori. We provide some examples illustrating the approach.