Unsupervised Domain Adaptation (DA) exploits the supervision of a label-rich
source dataset to make predictions on an unlabeled target dataset by aligning
the two data distributions. In robotics, DA is used to take advantage of
automatically generated synthetic data, that come with "free" annotation, to
make effective predictions on real data. However, existing DA methods are not
designed to cope with the multi-modal nature of RGB-D data, which are widely
used in robotic vision. We propose a novel RGB-D DA method that reduces the
synthetic-to-real domain shift by exploiting the inter-modal relation between
the RGB and depth image. Our method consists of training a convolutional neural
network to solve, in addition to the main recognition task, the pretext task of
predicting the relative rotation between the RGB and depth image. To evaluate
our method and encourage further research in this area, we define two benchmark
datasets for object categorization and instance recognition. With extensive
experiments, we show the benefits of leveraging the inter-modal relations for
RGB-D DA.

本文提出了一种新的 RGB-D DA 方法，使用卷积神经网络通过解决预文本任务来减少合成与真实数据领域之间的差异，对 RGB-D 数据的实例识别和对象分类进行了评估，并展示了利用跨模态关系进行 RGB-D DA 的优势。