OoD-Pose: Camera Pose Regression From Out-of-Distribution Synthetic Views

In this paper, we address the problem of camera pose estimation in outdoor and indoor scenarios. We propose a relative pose regression method that can directly regress the camera pose from images with significantly higher accuracy than existing methods of the same class. We first investigate one of the main factors that limits the accuracy of relative pose regression, and then introduce a new approach that significantly improves the performance. Specifically, we propose a method to overcome the biased training data by a novel training technique. It generates poses, guided by a probability distribution of the training set, which are then used to synthesise new views for training. Lastly, we evaluate our approach on widely used benchmarks and show that it achieves significantly lower error compared to prior regression-based methods and retrieval techniques.