Improving image-based localization through increasing correct feature correspondences

Abstract

Image-based localization is to provide contextual information based on a query image. Current state-of-the-art methods use 3D Structure-from-Motion reconstruction model to aid in localizing the query image, either by 2D-to-3D matching or by 3D-to-2D matching. By adding camera pose estimation, the system can perform image localization more accurately. However, incorrect feature correspondences between the 2D image and 3D reconstruction remains the main reason for failures in image localization. In our paper, we introduce a new method, which adds features embedding, to reduce the incorrect feature correspondences. We do the query expansion to add correspondences, where the associated 3d point has a high probability to be found in the same camera as the seed set. Using the techniques described, the registration accuracy can be significantly improved. Experiments on several large image datasets have shown our methods to outperform most state-of-the-art methods. © 2013 Springer-Verlag.