Object recognition with stereo vision and geometric hashing

In this paper we demonstrate a method to recognize 3D objects and to estimate their pose. For that purpose we use a combination of stereo vision and geometric hashing. Stereo vision is used to generate a large number of 3D low level features, of which many are spurious because at that stage of the process the correspondence problem is not solved as yet. However, geometric hashing is used to discriminate the true features from the spurious one. Geometric hashing is also the basis of a voting mechanism for the recognition of the objects in the scene. The speed of the geometric hashing algorithm helps to overcome the computational burden imposed by the correspondence problem in stereo vision. We look at different hash strategies using both points and lines features and compare our 3D approach to a recognition system based on 2D features. Experiments show that, although our 3D approach generates much more spurious scene features, it is just as fast and more reliable than the 2D system.