Relative Pose Solvers using Monocular Depth

Abstract

We describe a novel approach for using deep-learned priors to estimate the pose of a camera and show that these priors can be efficiently and accurately used for robust relative pose estimation. We use an off-the-shelf monocular depth network to provide an estimation of up-to-scale depth per pixel, and propose three new methods for solving for relative pose as well as a new algorithm for homography estimation. The additional signal provided by the depths leads to efficient solvers that require fewer correspondences than traditional methods and provide accurate and robust pose estimation when combined with state-of-the-art robust estimators, e.g., Graph-Cut RANSAC. The algorithms are tested on more than 70,000 publicly available image pairs from the 1DSfM dataset. The accuracy of the proposed methods are comparable or better than the standard five-point algorithm, and the reduced number of necessary correspondences speed up the robust estimation procedure, sometimes by orders of magnitude.