3D OBJECT RECOGNITION FROM 2D IMAGES USING GEOMETRIC HASHING

In this paper, a general technique for model-based recognition is discussed, called Geometric Hashing. Its purpose is to identify an object in the scene, together with its position and orientation. This technique is based on an intensive preprocessing stage, done off-line, where transformation invariant features of the models are indexed into a hash table. This makes the actual recognition particularly efficient. The algorithm stands out for its high inherent parallelism and its ability to deal with occluded scenes. This paper focuses on the use of Geometric Hashing for the case of 3D object recognition from 2D images. An efficient method to represent a 3D model by its 2D projections is proposed. Results are presented of experiments on random data and 3D objects. It has been found that distinguishing between different types of features in a model or scene results in a very efficient implementation of Geometric Hashing using a multidimensional hash table. The filtering ratio of this scheme turns out to be high enough to allow reliable recognition with the correct feature correspondence between model and scene. The algorithm performed successfully in dealing with scenes with up to 50% of occlusion and performed at speeds in the order of one second on a SPARC station.