Minimal Perspective Autocalibraiton

Andrea Porfiri Dal Cin, Timothy Duff, Luca Magri, Tomas Pajdla

Abstract: We introduce a new family of minimal problems for re- construction from multiple views. Our primary focus is a novel approach to autocalibration, a long-standing prob- lem in computer vision. Traditional approaches to this problem, such as those based on Kruppa’s equations or the modulus constraint, rely explicitly on the knowledge of multiple fundamental matrices or a projective reconstruc- tion. In contrast, we consider a novel formulation involv- ing constraints on image points, the unknown depths of 3D points, and a partially specified calibration matrix K. For 2 and 3 views, we present a comprehensive taxonomy of minimal autocalibration problems obtained by relaxing some of these constraints. These problems are organized into classes according to the number of views and any as- sumed prior knowledge of K. Within each class, we deter- mine problems with the fewest—or a relatively small num- ber of—solutions. From this zoo of problems, we devise three practical solvers. Experiments with synthetic and real data and interfacing our solvers with COLMAP demon- strate that we achieve superior accuracy compared to state- of-the-art calibration methods. The code is available at github.com/andreadalcin/MinimalPerspectiveAutocalibration.