3D objects classification based on $P recogniser

In this paper, we propose a method for 3-dimensional (3D) model recognition based on 2-dimensional (2D) views. The goal of this method is to provide a selection of 2D views from a 3D model, by using the $P method for 3D model retrieval from these views. So, in order to extract the necessary information, we study the different multi-view indexing methods, characterising the shape of the 3D image using 2D projection. With regard to the shape descriptor, we propose using the fast Fourier transform to provide spectral rendering for each extracted view. The method is based on the $P point-cloud recogniser. Our approach allows comparing either directly with a query image or with another 3D object by comparing their sets of views. We demonstrate the potential of this approach in a set of experiments, which prove that our system achieves a recognition rate ranging from 91.5% to 93.5%.