Self-supervised deep fisheye image rectification approach using coordinate relations

Masaki Hosono, Edgar Simo-Serra, Tomonari Sonoda

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings of MVA 2021 - 17th International Conference on Machine Vision Applications
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9784901122207
DOI
出版ステータスPublished - 2021 7月 25
イベント17th International Conference on Machine Vision Applications, MVA 2021 - Aichi, Japan
継続期間: 2021 7月 252021 7月 27

出版物シリーズ

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

Conference

Conference17th International Conference on Machine Vision Applications, MVA 2021
国/地域Japan
CityAichi
Period21/7/2521/7/27

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • コンピュータ ビジョンおよびパターン認識
  • 信号処理

フィンガープリント

「Self-supervised deep fisheye image rectification approach using coordinate relations」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル