Abstract
This paper presents a method of recognizing objects and estimating their 3-D poses from a monocular image. This method integrates image edge points and a 3-D edge model into an object model. The method retrieves candidate objects from an object database using image edge points with SIFT feature vectors. Then, the method estimates the 3-D pose of each candidate object by minimizing the re-projection errors of the 3-D edge model. Experimental results show that the method successfully recognized non-textured objects and complex-shaped objects in real environments.
