A quasiconvex formulation for radial cameras
Paper in proceeding, 2021

In this paper we study structure from motion problems for 1D radial cameras. Under this model the projection of a 3D point is a line in the image plane going through the principal point, which makes the model invariant to radial distortion and changes in focal length. It can therefore effectively be applied to uncalibrated image collections without the need for explicit estimation of camera intrinsics. We show that the reprojection errors of 1D radial cameras are examples of quasiconvex functions. This opens up the possibility to solve a general class of relevant reconstruction problems globally optimally using tools from convex optimization. In fact, our resulting algorithm is based on solving a series of LP problems. We perform an extensive experimental evaluation, on both synthetic and real data, showing that a whole class of multiview geometry problems across a range of different cameras models with varying and unknown intrinsic calibration can be reliably and accurately solved within the same framework.

Author

Carl Olsson

Computer vision and medical image analysis

Lund University

Viktor Larsson

Swiss Federal Institute of Technology in Zürich (ETH)

Fredrik Kahl

Computer vision and medical image analysis

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

10636919 (ISSN)

14571-14580
9781665445092 (ISBN)

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021
Virtual, Online, USA,

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Subject Categories

Computational Mathematics

Control Engineering

Computer Vision and Robotics (Autonomous Systems)

DOI

10.1109/CVPR46437.2021.01434

More information

Latest update

2/1/2022 9