Matching 2D Images in 3D: Metric Relative Pose from Metric
  Correspondences
Axel Barroso-Laguna Sowmya Munukutla Victor Adrian Prisacariu Eric Brachmann
Given two images, we can estimate the relative camera pose between them by
establishing image-to-image correspondences. Usually, correspondences are
2D-to-2D and the pose we estimate is defined only up to scale. Some
applications, aiming at instant augmented reality anywhere, require
scale-metric pose estimates, and hence, they rely on external depth estimators
to recover the scale. We present MicKey, a keypoint matching pipeline that is
able to predict metric correspondences in 3D camera space. By learning to match
3D coordinates across images, we are able to infer the metric relative pose
without depth measurements. Depth measurements are also not required for
training, nor are scene reconstructions or image overlap information. MicKey is
supervised only by pairs of images and their relative poses. MicKey achieves
state-of-the-art performance on the Map-Free Relocalisation benchmark while
requiring less supervision than competing approaches.