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Structure-From-Motion with a Non-Parametric Camera Model

Wang, Yihan ; Pan, Linfei ; Pollefeys, Marc and Larsson, Viktor LU (2025) 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2025 In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition p.1040-1049
Abstract

In this paper, we present a new generic Structure-From-Motion pipeline, GenSfM, that uses a non-parametric camera projection model. The model is self-calibrated during the reconstruction process and can fit a wide variety of cameras, ranging from simple low-distortion pinhole cameras to more extreme optical systems such as fisheye or catadioptric cameras. The key component in our framework is an adaptive calibration procedure that can estimate partial calibrations, only modeling regions of the image where sufficient constraints are available. In experiments, we show that our method achieves comparable accuracy to traditional Structure-From-Motion pipelines in easy scenarios, and outperforms them in cases where they are unable to... (More)

In this paper, we present a new generic Structure-From-Motion pipeline, GenSfM, that uses a non-parametric camera projection model. The model is self-calibrated during the reconstruction process and can fit a wide variety of cameras, ranging from simple low-distortion pinhole cameras to more extreme optical systems such as fisheye or catadioptric cameras. The key component in our framework is an adaptive calibration procedure that can estimate partial calibrations, only modeling regions of the image where sufficient constraints are available. In experiments, we show that our method achieves comparable accuracy to traditional Structure-From-Motion pipelines in easy scenarios, and outperforms them in cases where they are unable to self-calibrate their parametric models. Code is at https://github.com/Ivonne320/GenSfM.git

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author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
camera pose estimation, non-parametric camera model, sfm, triangulation
host publication
2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
series title
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
pages
10 pages
conference name
2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2025
conference location
Nashville, United States
conference dates
2025-06-11 - 2025-06-15
external identifiers
  • scopus:105017068129
ISSN
1063-6919
ISBN
979-8-3315-4364-8
DOI
10.1109/CVPR52734.2025.00105
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2025 IEEE.
id
690bad4b-0988-4871-a56e-aa52531d79cc
date added to LUP
2025-12-08 14:50:56
date last changed
2025-12-09 08:23:57
@inproceedings{690bad4b-0988-4871-a56e-aa52531d79cc,
  abstract     = {{<p>In this paper, we present a new generic Structure-From-Motion pipeline, GenSfM, that uses a non-parametric camera projection model. The model is self-calibrated during the reconstruction process and can fit a wide variety of cameras, ranging from simple low-distortion pinhole cameras to more extreme optical systems such as fisheye or catadioptric cameras. The key component in our framework is an adaptive calibration procedure that can estimate partial calibrations, only modeling regions of the image where sufficient constraints are available. In experiments, we show that our method achieves comparable accuracy to traditional Structure-From-Motion pipelines in easy scenarios, and outperforms them in cases where they are unable to self-calibrate their parametric models. Code is at https://github.com/Ivonne320/GenSfM.git</p>}},
  author       = {{Wang, Yihan and Pan, Linfei and Pollefeys, Marc and Larsson, Viktor}},
  booktitle    = {{2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}},
  isbn         = {{979-8-3315-4364-8}},
  issn         = {{1063-6919}},
  keywords     = {{camera pose estimation; non-parametric camera model; sfm; triangulation}},
  language     = {{eng}},
  pages        = {{1040--1049}},
  series       = {{Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition}},
  title        = {{Structure-From-Motion with a Non-Parametric Camera Model}},
  url          = {{http://dx.doi.org/10.1109/CVPR52734.2025.00105}},
  doi          = {{10.1109/CVPR52734.2025.00105}},
  year         = {{2025}},
}