  Technological development aims to produce generations of increasingly
efficient robots able to perform complex tasks. This requires considerable
efforts, from the scientific community, to find new algorithms that solve
computer vision problems, such as object recognition. The diffusion of RGB-D
cameras directed the study towards the research of new architectures able to
exploit the RGB and Depth information. The project that is developed in this
thesis concerns the realization of a new end-to-end architecture for the
recognition of RGB-D objects called RCFusion. Our method generates compact and
highly discriminative multi-modal features by combining complementary RGB and
depth information representing different levels of abstraction. We evaluate our
method on standard object recognition datasets, RGB-D Object Dataset and
JHUIT-50. The experiments performed show that our method outperforms the
existing approaches and establishes new state-of-the-art results for both
datasets.
