What Correspondences Reveal About Unknown Camera and Motion Models?

In two-view geometry, camera models and motion types are used as key knowledge along with the image point correspondences in order to solve several key problems of 3D vision. Problems such as Structure-from-Motion (SfM) and camera self-calibration are tackled under the assumptions of a specific camera projection model and motion type. However, these key assumptions may not be always justified, i.e.., we may often know neither the camera model nor the motion type beforehand. In that context, one can extract only the point correspondences between images. From such correspondences, recovering two-view relationship --expressed by the unknown camera model and motion type-- remains to be an unsolved problem. In this paper, we tackle this problem in two steps. First, we propose a method that computes the correct two-view relationship in the presence of noise and outliers. Later, we study different possibilities to disambiguate the obtained relationships into camera model and motion type. By extensive experiments on both synthetic and real data, we verify our theory and assumptions in practical settings.