  Conventional 2D Convolutional Neural Networks (CNN) extract features from an
input image by applying linear filters. These filters compute the spatial
coherence by weighting the photometric information on a fixed neighborhood
without taking into account the geometric information. We tackle the problem of
improving the classical RGB CNN methods by using the depth information provided
by the RGB-D cameras. State-of-the-art approaches use depth as an additional
channel or image (HHA) or pass from 2D CNN to 3D CNN. This paper proposes a
novel and generic procedure to articulate both photometric and geometric
information in CNN architecture. The depth data is represented as a 2D offset
to adapt spatial sampling locations. The new model presented is invariant to
scale and rotation around the X and the Y axis of the camera coordinate system.
Moreover, when depth data is constant, our model is equivalent to a regular
CNN. Experiments of benchmarks validate the effectiveness of our model.
