A quasiconvex formulation for radial cameras
Paper i 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.


Carl Olsson

Datorseende och medicinsk bildanalys

Lunds universitet

Viktor Larsson

Eidgenössische Technische Hochschule Zürich (ETH)

Fredrik Kahl

Datorseende och medicinsk bildanalys

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

10636919 (ISSN)

9781665445092 (ISBN)

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

Integrering av geometri och semantik i datorseende

Vetenskapsrådet (VR) (2016-04445), 2017-01-01 -- 2020-12-31.

Optimeringsmetoder med prestandagarantier för maskininlärningsmetoder

Vetenskapsrådet (VR) (2018-05375), 2019-01-01 -- 2022-12-31.




Datorseende och robotik (autonoma system)



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