Self-supervised deep fisheye image rectification approach using coordinate relations

Masaki Hosono, Edgar Simo-Serra, Tomonari Sonoda

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With the ascent of wearable camera, dashcam, and autonomous vehicle technology, fisheye lens cameras are becoming more widespread. Unlike regular cameras, the videos and images taken with fisheye lens suffer from significant lens distortion, thus having detrimental effects on image processing algorithms. When the camera parameters are known, it is straight-forward to correct the distortion, however, without known camera parameters, distortion correction becomes a non-trivial task. While learning-based approaches exist, they rely on complex datasets and have limited generalization. In this work, we propose a CNN-based approach that can be trained with readily available data. We exploit the fact that relationships between pixel coordinates remain stable after homogeneous distortions to design an efficient rectification model. Experiments performed on the cityscapes dataset show the effectiveness of our approach. Our code is available at GitHub11https://github.com/MasakHosono/SelfSupervisedFisheyeRectification.

Original languageEnglish
Title of host publicationProceedings of MVA 2021 - 17th International Conference on Machine Vision Applications
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9784901122207
DOIs
Publication statusPublished - 2021 Jul 25
Event17th International Conference on Machine Vision Applications, MVA 2021 - Aichi, Japan
Duration: 2021 Jul 252021 Jul 27

Publication series

NameProceedings of MVA 2021 - 17th International Conference on Machine Vision Applications

Conference

Conference17th International Conference on Machine Vision Applications, MVA 2021
Country/TerritoryJapan
CityAichi
Period21/7/2521/7/27

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Signal Processing

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