# A Comprehensive Overview of Fish-Eye Camera Distortion Correction Methods

arXiv: https://arxiv.org/abs/2401.00442 · HTML: https://mlsanity.com/paper/2401.00442/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 60 references that are in the map.
- First posted on arXiv 31 Dec 2023.

## Builds on (most cited first, 14 of 14)

- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Semantic Understanding of Scenes through the ADE20K Dataset](https://mlsanity.com/paper/1608.05442/) (2016)
- [ViTAEv2: Vision Transformer Advanced by Exploring Inductive Bias for Image Recognition and Beyond](https://mlsanity.com/paper/2202.10108/) (2022)
- [Learning to Parse Wireframes in Images of Man-Made Environments](https://mlsanity.com/paper/2007.07527/) (2020)
- [Progressively Complementary Network for Fisheye Image Rectification Using Appearance Flow](https://mlsanity.com/paper/2103.16026/) (2021)
- [Neural Ray Surfaces for Self-Supervised Learning of Depth and Ego-motion](https://mlsanity.com/paper/2008.06630/) (2020)
- [DeepCorrect: Correcting DNN models against Image Distortions](https://mlsanity.com/paper/1705.02406/) (2017)
- [SimFIR: A Simple Framework for Fisheye Image Rectification with Self-supervised Representation Learning](https://mlsanity.com/paper/2308.09040/) (2023)
- [Rethinking Generic Camera Models for Deep Single Image Camera Calibration to Recover Rotation and Fisheye Distortion](https://mlsanity.com/paper/2111.12927/) (2021)
- [Fisheye Distortion Rectification from Deep Straight Lines](https://mlsanity.com/paper/2003.11386/) (2020)
- [SIR: Self-supervised Image Rectification via Seeing the Same Scene from Multiple Different Lenses](https://mlsanity.com/paper/2011.14611/) (2020)
- [Woodscape Fisheye Object Detection for Autonomous Driving -- CVPR 2022 OmniCV Workshop Challenge](https://mlsanity.com/paper/2206.12912/) (2022)
- [Spatiotemporal Deformation Perception for Fisheye Video Rectification](https://mlsanity.com/paper/2302.03934/) (2023)
- [A Stronger Stitching Algorithm for Fisheye Images based on Deblurring and Registration](https://mlsanity.com/paper/2307.11997/) (2023)

## Built on by (most cited first, 3 of 3)

- [Edge-case Synthesis for Fisheye Object Detection: A Data-centric Perspective](https://mlsanity.com/paper/2507.16254/) (2025)
- [Accelerating Vehicle Routing via AI-Initialized Genetic Algorithms](https://mlsanity.com/paper/2504.06126/) (2025)
- [Beyond Reprojection Error: Camera Calibration with 3D Targets](https://mlsanity.com/paper/2608.05066/) (2026)

## Abstract (arXiv)

The fisheye camera, with its unique wide field of view and other characteristics, has found extensive applications in various fields. However, the fisheye camera suffers from significant distortion compared to pinhole cameras, resulting in distorted images of captured objects. Fish-eye camera distortion is a common issue in digital image processing, requiring effective correction techniques to enhance image quality. This review provides a comprehensive overview of various methods used for fish-eye camera distortion correction. The article explores the polynomial distortion model, which utilizes polynomial functions to model and correct radial distortions. Additionally, alternative approaches such as panorama mapping, grid mapping, direct methods, and deep learning-based methods are discussed. The review highlights the advantages, limitations, and recent advancements of each method, enabling readers to make informed decisions based on their specific needs.