Globally Optimal Inlier Set Maximization With Unknown Rotation and Focal Length

Identifying inliers and outliers among data is a fundamental problem for model estimation. This paper considers models composed of rotation and focal length, which typically occurs in the context of panoramic imaging. An efficient approach consists in computing the underlying model such that the number of inliers is maximized. The most popular tool for inlier set maximization must be RANSAC and its numerous variants. While they can provide interesting results, they are not guaranteed to return the globally optimal solution, i.e. the model leading to the highest number of inliers. We propose a novel globally optimal approach based on branch-and-bound. It computes the rotation and the focal length maximizing the number of inlier correspondences and considers the reprojection error in the image space. Our approach has been successfully applied on synthesized data and real images.