Fast 3D Object Recognition of Rotationally Symmetric Objects

In this paper we extend a recent approach for 3D object recognition in order to deal with rotationally symmetric objects, which are frequent in daily environments. We base our work in a recent method that represents objects using a hash table of shape features, which in the case of symmetric objects contains redundant information. We propose a way to remove redundant features by adding a weight factor for each set of symmetric features. The removal procedure leads to significant computational savings while keeping the recognition perfomance. The experiments show recognition time improvements up to 300x with respect to state-of-the-art methods.