Minimal Perspective Autocalibration
Andrea Porfiri Dal Cin, Timothy Duff, Luca Magri, Tomas Pajdla

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 . For and 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 . Within each class, we determine problems with the fewest -- or a relatively…
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Taxonomy
TopicsImage Processing Techniques and Applications · Image and Object Detection Techniques
MethodsFocus
