  In this paper, we address the problem of camera pose estimation in outdoor
and indoor scenarios. In comparison to the currently top-performing methods
that rely on 2D to 3D matching, we propose a model that can directly regress
the camera pose from images with significantly higher accuracy than existing
methods of the same class. We first analyse why regression methods are still
behind the state-of-the-art, and we bridge the performance gap with our new
approach. Specifically, we propose a way to overcome the biased training data
by a novel training technique, which generates poses guided by a probability
distribution from the training set for synthesising new training views. Lastly,
we evaluate our approach on two widely used benchmarks and show that it
achieves significantly improved performance compared to prior regression-based
methods, retrieval techniques as well as 3D pipelines with local feature
matching.
