  Scene classification is one of the basic problems in computer vision research
with extensive applications in robotics. When available, depth images provide
helpful geometric cues that complement the RGB texture information and help to
identify more discriminative scene image features. Depth sensing technology
developed fast in the last years and a great variety of 3D cameras have been
introduced, each with different acquisition properties. However, when targeting
big data collections, often multi-modal images are gathered disregarding their
original nature. In this work we put under the spotlight the existence of a
possibly severe domain shift issue within multi-modality scene recognition
datasets. We design an experimental testbed to study this problem and present a
method based on self-supervised inter-modality translation able to adapt across
different camera domains. Our extensive experimental analysis confirms the
effectiveness of the proposed approach.
