Scene recognition 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 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, those
properties are often neglected when targeting big data collections, so
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. As a consequence,
a scene classification model trained on one camera may not generalize on data
from a different camera, only providing a low recognition performance. Starting
from the well-known SUN RGB-D dataset, we designed an experimental testbed to
study this problem and we use it to benchmark the performance of existing
methods. Finally, we introduce a novel adaptive scene recognition approach that
leverages self-supervised translation between modalities. Indeed, learning to
go from RGB to depth and vice-versa is an unsupervised procedure that can be
trained jointly on data of multiple cameras and may help to bridge the gap
among the extracted feature distributions. Our experimental results confirm the
effectiveness of the proposed approach.

本文提出了一种自适应景观识别方法，该方法使用 RGB 和深度图像之间的自监督转换，以解决多模态数据集之间的领域转移问题，并证明其在不同相机采集的数据之间具有很强的泛化能力。