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on Camera Calibration","Deep Learning Based","Datasets","Related Libraries"],"sub_categories":[],"readme":"# Awesome Image Rectification \nA curated list of image rectification (distortion correction) and camera calibration papers.\n\n## Table of Contents\n- Deep Learning Based Methods\n- Traditional Methods\n- Datasets\n- Material\n- Related Papers on Camera Calibration\n- Related Libraries\n\n## Deep Learning Based\n\n\u003e★ means that it is recommended for reading.\n\n|Paper|Supervised|Link|Conference|Notes|\n|---|---|---|---|---|\n|Blind Geometric Distortion Correction on Images Through Deep Learning ★|Yes|[Link](http://openaccess.thecvf.com/content_CVPR_2019/papers/Li_Blind_Geometric_Distortion_Correction_on_Images_Through_Deep_Learning_CVPR_2019_paper.pdf)|CVPR '19|Parameterized distortions (barrel, pincushion, and etc)|\n|ESIR: End-to-end Scene Text Recognition via Iterative Image Rectification||[Link](https://arxiv.org/pdf/1812.05824.pdf)|CVPR '19|Scene text distortion|\n|Arbitrary Shape Scene Text Detection with Adaptive Text Region Representation||[Link](http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Arbitrary_Shape_Scene_Text_Detection_With_Adaptive_Text_Region_Representation_CVPR_2019_paper.pdf)|CVPR '19|Scene text recognition|\n|Towards Robust Curve Text Detection with Conditional Spatial Expansion||[Link](http://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_Towards_Robust_Curve_Text_Detection_With_Conditional_Spatial_Expansion_CVPR_2019_paper.pdf)|CVPR '19|Scene text recognition|\n|Learning to Calibrate Straight Lines for Fisheye Image Rectification ★||[Link](http://openaccess.thecvf.com/content_CVPR_2019/papers/Xue_Learning_to_Calibrate_Straight_Lines_for_Fisheye_Image_Rectification_CVPR_2019_paper.pdf)|CVPR '19|Fish eye distortion, straight line calibration|\n|Learning Structure-And-Motion-Aware Rolling Shutter Correction|Yes|[Link](http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhuang_Learning_Structure-And-Motion-Aware_Rolling_Shutter_Correction_CVPR_2019_paper.pdf)|CVPR '19|Rolling shutter distortion|\n|Symmetry-constrained Rectification Network for Scene Text Recognition||[Link](https://arxiv.org/pdf/1908.01957.pdf)|ICCV '19|Scene text distortion|\n|DR-GAN: Automatic Radial Distortion Rectification Using Conditional GAN in Real-Time|Yes|[Link](https://ieeexplore.ieee.org/document/8636975)|IEEE Transactions on Circuits and Systems for Video Technology'19|Conditional GAN.Learn the mapping between distorted and clean images.No hand-crafted technique.|\n|FishEyeRecNet: A Multi-Context Collaborative Deep Network for Fisheye Image Rectification ★|Yes|[Link](https://arxiv.org/pdf/1804.04784.pdf)|ECCV '18|Fish eye distortion, predicts camera model parameters|\n|MORAN: A Multi-Object Rectified Attention Network for Scene Text Recognition||[Link](https://arxiv.org/pdf/1901.03003.pdf)||Scene text distortion|\n|GridFace: Face Rectification via Learning Local Homography Transformations||[Link](https://arxiv.org/pdf/1808.06210.pdf)|ECCV '18|Face rectification|\n|Semantic Segmentation of Fisheye Images||[Link](http://openaccess.thecvf.com/content_ECCVW_2018/papers/11129/Blott_Semantic_Segmentation_of_Fisheye_Images_ECCVW_2018_paper.pdf)|ECCV '18 Workshop|Sementic segmentation for fish-eye images|\n|Distortion-aware CNNs for Spherical Images||[Link](https://www.ijcai.org/proceedings/2018/0167.pdf)|IJCAI '18|Spherical distortion|\n|Fingerprint Distortion Rectification using Deep Convolutional Neural Networks||[Link](https://arxiv.org/pdf/1801.01198.pdf)|ICB '18|Fingerprint distortion|\n|CNN-based Fisheye Image Real-Time Semantic Segmentation||[Link](http://www.robesafe.es/personal/bergasa/papers/iv2018_cnn-fisheye.pdf)|IEEE Intelligent Vehicles Symposium (IV) '18|Sementic segmentation on fish-eye images|\n|Deep View Morphing||[Link](https://arxiv.org/pdf/1703.02168.pdf)|CVPR '17|3D view morphing|\n|Robust Scene Text Recognition with Automatic Rectification||[Link](https://arxiv.org/pdf/1603.03915.pdf)|CVPR '16|Scene text distortion|\n|Radial Lens Distortion Correction Using Convolutional Neural Networks Trained with Synthesized Images||[Link](\u003chttps://link.springer.com/chapter/10.1007/978-3-319-54187-7_3\u003e)|ACCV '16|Radial lens distortion|\n\n## Traditional\n|Paper|Link|Conference|Notes|\n|---|---|---|---|\n|A Robust Method for Strong Rolling Shutter Effects Correction Using Lines with Automatic Feature Selection|[Link](http://openaccess.thecvf.com/content_cvpr_2018/papers/Lao_A_Robust_Method_CVPR_2018_paper.pdf)|CVPR '19|Rolling shutter distortion|\n|Distortion-Free Wide-Angle Portraits on Camera Phones|[Link](http://people.csail.mit.edu/yichangshih/wide_angle_portrait/shih_sig19.pdf)|SIGGRAPH '19|Wide-angle camera, fish eye distortion|\n|Restoration of Non-rigidly Distorted Underwater Images using a Combination of Compressive Sensing and Local Polynomial Image Representations|[Link](https://arxiv.org/pdf/1908.01940.pdf)|ICCV '19|Underwater image distortion|\n|Rolling-Shutter-Aware Differential SfM and Image Rectification|[Link](http://openaccess.thecvf.com/content_ICCV_2017/papers/Zhuang_Rolling-Shutter-Aware_Differential_SfM_ICCV_2017_paper.pdf)|ICCV '17|Rolling shutter artifact correction|\n|From Bows to Arrows: Rolling Shutter Rectification of Urban Scenes|[Link](http://openaccess.thecvf.com/content_cvpr_2016/papers/Rengarajan_From_Bows_to_CVPR_2016_paper.pdf)|CVPR '16|Rolling shutter artifact correction|\n|Line-Based Multi-Label Energy Optimization for Fisheye Image Rectification and Calibration|[Link](https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Zhang_Line-Based_Multi-Label_Energy_2015_CVPR_paper.pdf)|CVPR '15|Fish eye distortion|\n|Radial lens distortion correction using cascaded one-parameter division model|[Link](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=7351478)|ICIP '15|Radial distortion|\n|Lens Distortion Rectification using Triangulation based Interpolation|[Link](https://arxiv.org/pdf/1611.09559.pdf)|ISVC '15|Lens distortion|\n|Radial distortion correction from a single image of a planar calibration pattern using convex optimization|[Link](https://projet.liris.cnrs.fr/imagine/pub/proceedings/ICIP-2014/Papers/1569891193.pdf)|ICIP '14|Radial distortion|\n|Unsupervised Intrinsic Calibration from a Single Frame Using a \"Plumb-Line\" Approach|[Link](https://www.researchgate.net/publication/258385936_Unsupervised_Intrinsic_Calibration_from_a_Single_Frame_Using_a_Plumb-Line_Approach)|ICCV '13|Line detection approach|\n|Radially-Distorted Conjugate Translations|[Link](https://arxiv.org/pdf/1711.11339.pdf)||Radial distortion|\n|Pixel-variant Local Homography for Fisheye Stereo Rectification Minimizing Resampling Distortion|[Link](https://arxiv.org/pdf/1707.03775.pdf)||Radial Distortion|\n|Rectification from Radially-Distorted Scales|[Link](https://arxiv.org/pdf/1807.06110.pdf)||Radial distortion|\n|**Auto- matic lens distortion correction using one-parameter division models**|[Link](https://pdfs.semanticscholar.org/85be/954dcea4cdebbfbee55143a6e605ad813b45.pdf)||Radial distortion|\n|**Automatic Radial Distortion Estimation from a Single Image**|[Link](http://www.cs.ait.ac.th/vgl/faisal/paper/JMIV-Paper.pdf)||Radial Distortion|\n\n## Datasets\n|Paper|Paper Link|Dataset Link|Synthesized|Annotations|\n|---|---|---|---|---|\n|Parameterized Synthetic Image Data Set for Fisheye Lens|[Link](https://arxiv.org/pdf/1811.04627.pdf)|[Link]( http://www2.leuphana.de/misl/fisheye-data-set/)|Yes|\n|Learning to Calibrate Straight Lines for Fisheye Image Rectification|[Link](https://arxiv.org/pdf/1904.09856.pdf)||No|Distortion parameters and distorted lines|\n\n## Material\n\n| Name                                                         | Course Link                                                  | Note                                     |\n| ------------------------------------------------------------ | ------------------------------------------------------------ | ---------------------------------------- |\n| CS 131 Computer Vision: Foundations and Applications (Fall 2015-2016) | [Link](\u003chttp://vision.stanford.edu/teaching/cs131_fall1516/\u003e) | Cover the basic concept of camera model. |\n| Review of Geometric Distortion Compensation in Fish-Eye Cameras | [Link](\u003chttp://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.140.4542\u0026rep=rep1\u0026type=pdf\u003e) | An overview of common fish eye models.   |\n\n## Papers on Camera Calibration\n\n|Paper|Link|Conference|Note|\n|---|---|---|---|\n|Deep Single Image Camera Calibration with Radial Distortion|[Link](http://openaccess.thecvf.com/content_CVPR_2019/papers/Lopez_Deep_Single_Image_Camera_Calibration_With_Radial_Distortion_CVPR_2019_paper.pdf)|CVPR '19|Reduce parameters from 6 to 4. Predict parameters by Simple CNN.|\n|Parameter-free Lens Distortion Calibration of Central Cameras|[Link](http://openaccess.thecvf.com/content_ICCV_2017/papers/Bergamasco_Parameter-Free_Lens_Distortion_ICCV_2017_paper.pdf)|ICCV '17||\n|Unsupervised Vanishing Point Detection and Camera Calibration from a Single Manhattan Image with Radial Distortion|[Link](http://openaccess.thecvf.com/content_cvpr_2017/papers/Antunes_Unsupervised_Vanishing_Point_CVPR_2017_paper.pdf)|CVPR '17||\n\n## Related Libraries\n|Library|Link|Notes|\n|---|---|---|\n|Augmentor|[Link](https://github.com/mdbloice/Augmentor)|Image data augmentation by distortion|\n\n## Contributing \nPR me related papers!\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/bchao1%2Fawesome-image-rectification/projects"}