REVERSE DOMAIN ADAPTATION FOR INDOOR CAMERA POSE REGRESSION

Abstract

Synthetic images have been used to mitigate the scarcity of annotated data for training deep learning approaches, followed by domain adaptation that reduces the gap between synthetic and real images. One such approach is using Generative Adversarial Networks (GANs) such as CycleGAN to bridge the domain gap where the synthetic images are translated into real-looking synthetic images that are used to train the deep learning models. In this article, we explore the less intuitive alternate strategy for domain adaption in the reverse direction; i.e., real-to-synthetic adaptation. We train the deep learning models with synthetic data directly, and then during inference we apply domain adaptation t..