Metadata only
Date
2022Type
- Conference Paper
ETH Bibliography
yes
Altmetrics
Abstract
Low-dimensional parametric models are the de-facto standard in computer vision for intrinsic camera calibration. These models explicitly describe the mapping between incoming viewing rays and image pixels. In this paper, we explore an alternative approach which implicitly models the lens distortion. The main idea is to replace the parametric model with a regularization term that ensures the latent distortion map varies smoothly throughout the image. The proposed model is effectively parameter-free and allows us to optimize the 6 degree-of-freedom camera pose without explicitly knowing the intrinsic calibration. We show that the method is applicable to a wide selection of cameras with varying distortion and in multiple applications, such as visual localization and structure-from-motion. Show more
Publication status
publishedExternal links
Book title
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Pages / Article No.
Publisher
IEEEEvent
Subject
3D from multi-view and sensorsOrganisational unit
03766 - Pollefeys, Marc / Pollefeys, Marc
More
Show all metadata
ETH Bibliography
yes
Altmetrics