  Convolutional Neural Networks (CNNs) trained on large scale RGB databases
have become the secret sauce in the majority of recent approaches for object
categorization from RGB-D data. Thanks to colorization techniques, these
methods exploit the filters learned from 2D images to extract meaningful
representations in 2.5D. Still, the perceptual signature of these two kind of
images is very different, with the first usually strongly characterized by
textures, and the second mostly by silhouettes of objects. Ideally, one would
like to have two CNNs, one for RGB and one for depth, each trained on a
suitable data collection, able to capture the perceptual properties of each
channel for the task at hand. This has not been possible so far, due to the
lack of a suitable depth database. This paper addresses this issue, proposing
to opt for synthetically generated images rather than collecting by hand a 2.5D
large scale database. While being clearly a proxy for real data, synthetic
images allow to trade quality for quantity, making it possible to generate a
virtually infinite amount of data. We show that the filters learned from such
data collection, using the very same architecture typically used on visual
data, learns very different filters, resulting in depth features (a) able to
better characterize the different facets of depth images, and (b) complementary
with respect to those derived from CNNs pre-trained on 2D datasets. Experiments
on two publicly available databases show the power of our approach.
