6-DOF Image Localization From Massive Geo-Tagged Reference Images

The 6-degrees of freedom (DOF) image localization, which aims to calculate the spatial position and rotation of a camera, is a challenging problem for most location-based services. In existing approaches, this problem is often tackled by finding the matches between 2D image points and 3D structure points so as to derive the location information via direct linear transformation algorithm. However, as these 2D-to-3D-based approaches need to reconstruct the 3D structure points of the scene, they may not be flexible enough to employ massive and increasing geo-tagged data. To this end, this paper presents a novel approach for 6-DOF image localization by fusing candidate poses relative to reference images. In this approach, we propose to localize an input image according to the position and rotation information of multiple geo-tagged images retrieved from a reference dataset. From the reference images, an efficient relative pose estimation algorithm is proposed to derive a set of candidate poses for the input image. Each candidate pose encodes the relative rotation and direction of the input image with respect to a specific reference image. Finally, these candidate poses can be fused together by minimizing a well-defined geometry error so that the 6-DOF location of the input image is effectively derived. Experimental results show that our method can obtain satisfactory localization accuracy. In addition, the proposed relative pose estimation algorithm is much faster than existing work.