  Despite recent advances on the topic of direct camera pose regression using
neural networks, accurately estimating the camera pose of a single RGB image
still remains a challenging task. To address this problem, we introduce a novel
framework based, in its core, on the idea of implicitly learning the joint
distribution of RGB images and their corresponding camera poses using a
discriminator network and adversarial learning. Our method allows not only to
regress the camera pose from a single image, however, also offers a solely
RGB-based solution for camera pose refinement using the discriminator network.
Further, we show that our method can effectively be used to optimize the
predicted camera poses and thus improve the localization accuracy. To this end,
we validate our proposed method on the publicly available 7-Scenes dataset
improving upon the results of direct camera pose regression methods.
