DFNet: Enhance Absolute Pose Regression with Direct Feature Matching

We introduce a camera relocalization pipeline that combines absolute pose regression (APR) and direct feature matching. Existing photometric-based methods have trouble on scenes with large photometric distortions, e.g. outdoor environments. By incorporating an exposure-adaptive novel view synthesis, our methods can successfully address the challenges. Moreover, by introducing domain-invariant feature matching, our solution can improve pose regression accuracy while using semi-supervised learning on unlabeled data. In particular, the pipeline consists of two components, Novel View Synthesizer and FeatureNet (DFNet). The former synthesizes novel views compensating for changes in exposure and the latter regresses camera poses and extracts robust features that bridge the domain gap between real images and synthetic ones. We show that domain invariant feature matching effectively enhances camera pose estimation both in indoor and outdoor scenes. Hence, our method achieves a state-of-the-art accuracy by outperforming existing single-image APR methods by as much as 56%, comparable to 3D structure-based methods.