Minimal Perspective Autocalibration

TL;DR

This paper introduces a new family of minimal problems for camera autocalibration from multiple views, proposing novel formulations and practical solvers that outperform existing methods in accuracy.

Contribution

It presents a novel autocalibration formulation based on image points, depths, and partial calibration, with comprehensive problem taxonomy and efficient solvers.

Findings

Proposed solvers outperform state-of-the-art calibration methods.

Validated on synthetic and real data with superior accuracy.

Organized minimal problems into a comprehensive taxonomy.

Abstract

We introduce a new family of minimal problems for reconstruction from multiple views. Our primary focus is a novel approach to autocalibration, a long-standing problem 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 reconstruction. In contrast, we consider a novel formulation involving 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 assumed prior knowledge of $K$. Within each class, we determine problems with the fewest -- or a relatively small number of -- solutions. From this zoo of problems, we devise three practical solvers. Experiments with synthetic and real data and interfacing our solvers with COLMAP demonstrate that we achieve superior accuracy compared to state-of-the-art calibration methods. The code is available at https://github.com/andreadalcin/MinimalPerspectiveAutocalibration