In this paper we describe our 3D object signature for 3D object classification. The signature is based on a learning approach
that finds salient points on a 3D object and represent these points in a 2D spatial map based on a longitude-latitude transformation.
Experimental results show high classification rates on both pose-normalized and rotated objects and include a study on classification
accuracy as a function of number of rotations in the training set.
Keywords 3D Object Classification - 3D Object Signature