# Learning to Calibrate Straight Lines for Fisheye Image Rectification

arXiv: https://arxiv.org/abs/1904.09856 · HTML: https://mlsanity.com/paper/1904.09856/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2019.

## Built on by (most cited first, 23 of 23)

- [Towards Light-weight and Real-time Line Segment Detection](https://mlsanity.com/paper/2106.00186/) (2021)
- [SRWarp: Generalized Image Super-Resolution under Arbitrary Transformation](https://mlsanity.com/paper/2104.10325/) (2021)
- [Revisiting Document Image Dewarping by Grid Regularization](https://mlsanity.com/paper/2203.16850/) (2022)
- [Progressively Complementary Network for Fisheye Image Rectification Using Appearance Flow](https://mlsanity.com/paper/2103.16026/) (2021)
- [Deep Learning for Camera Calibration and Beyond: A Survey](https://mlsanity.com/paper/2303.10559/) (2023)
- [SimFIR: A Simple Framework for Fisheye Image Rectification with Self-supervised Representation Learning](https://mlsanity.com/paper/2308.09040/) (2023)
- [DarSwin: Distortion Aware Radial Swin Transformer](https://mlsanity.com/paper/2304.09691/) (2023)
- [FishFormer: Annulus Slicing-based Transformer for Fisheye Rectification with Efficacy Domain Exploration](https://mlsanity.com/paper/2207.01925/) (2022)
- [RecRecNet: Rectangling Rectified Wide-Angle Images by Thin-Plate Spline Model and DoF-based Curriculum Learning](https://mlsanity.com/paper/2301.01661/) (2023)
- [Rethinking Generic Camera Models for Deep Single Image Camera Calibration to Recover Rotation and Fisheye Distortion](https://mlsanity.com/paper/2111.12927/) (2021)
- [Exploiting the Distortion-Semantic Interaction in Fisheye Data](https://mlsanity.com/paper/2305.00079/) (2023)
- [A Comprehensive Review of Image Line Segment Detection and Description: Taxonomies, Comparisons, and Challenges](https://mlsanity.com/paper/2305.00264/) (2023)
- [Dual Diffusion Architecture for Fisheye Image Rectification: Synthetic-to-Real Generalization](https://mlsanity.com/paper/2301.11785/) (2023)
- [Dur360BEV: A Real-world 360-degree Single Camera Dataset and Benchmark for Bird-Eye View Mapping in Autonomous Driving](https://mlsanity.com/paper/2503.00675/) (2025)
- [Spatiotemporal Deformation Perception for Fisheye Video Rectification](https://mlsanity.com/paper/2302.03934/) (2023)
- [FishRecGAN: An End to End GAN Based Network for Fisheye Rectification and Calibration](https://mlsanity.com/paper/2305.05222/) (2023)
- [Rectifying homographies for stereo vision: analytical solution for minimal distortion](https://mlsanity.com/paper/2203.00123/) (2022)
- [DarSwin-Unet: Distortion Aware Encoder-Decoder Architecture](https://mlsanity.com/paper/2407.17328/) (2024)
- [QueryCDR: Query-Based Controllable Distortion Rectification Network for Fisheye Images](https://mlsanity.com/paper/2412.13496/) (2024)
- [Learning-Based Framework for Camera Calibration with Distortion Correction and High Precision Feature Detection](https://mlsanity.com/paper/2202.00158/) (2022)
- [RoFIR: Robust Fisheye Image Rectification Framework Impervious to Optical Center Deviation](https://mlsanity.com/paper/2406.18927/) (2024)
- [Rectification Reimagined: A Unified Mamba Model for Image Correction and Rectangling with Prompts](https://mlsanity.com/paper/2512.18718/) (2025)
- [Rolling Shutter Camera Self-Calibration](https://mlsanity.com/paper/2608.01509/) (2026)

## Abstract (arXiv)

This paper presents a new deep-learning based method to simultaneously calibrate the intrinsic parameters of fisheye lens and rectify the distorted images. Assuming that the distorted lines generated by fisheye projection should be straight after rectification, we propose a novel deep neural network to impose explicit geometry constraints onto processes of the fisheye lens calibration and the distorted image rectification. In addition, considering the nonlinearity of distortion distribution in fisheye images, the proposed network fully exploits multi-scale perception to equalize the rectification effects on the whole image. To train and evaluate the proposed model, we also create a new largescale dataset labeled with corresponding distortion parameters and well-annotated distorted lines. Compared with the state-of-the-art methods, our model achieves the best published rectification quality and the most accurate estimation of distortion parameters on a large set of synthetic and real fisheye images.