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--- visibility: public --- # Computer Vision **repo:** [jbhuang0604/awesome-computer-vision](https://github.com/jbhuang0604/awesome-computer-vision) **category:** [[computer-science|Computer Science]] **related:** [[deep-learning|Deep Learning]] · [[machine-learning|Machine Learning]] · [[python|Python]] · [[generative-ai|Generative Ai]] --- # Awesome Computer Vision: [](https://github.com/sindresorhus/awesome) A curated list of awesome computer vision resources, inspired by [awesome-php](https://github.com/ziadoz/awesome-php). For a list people in computer vision listed with their academic genealogy, please visit [here](https://github.com/jbhuang0604/awesome-computer-vision/blob/master/people.md) ## Contributing Please feel free to send me [pull requests](https://github.com/jbhuang0604/awesome-computer-vision/pulls) or email (jbhuang@vt.edu) to add links. ## Table of Contents - [Awesome Lists](#[awesome](/@harrisonqian/awesome/wiki/miscellaneous/awesome)-lists) - [Books](#books) - [Courses](#courses) - [Papers](#papers) - [Software](#software) - [Datasets](#datasets) - [Pre-trained Computer Vision Models](#Pre-trained-Computer-Vision-Models) - [Tutorials and Talks](#[tutorials](/@harrisonqian/awesome/wiki/computer-science/tutorials)-and-talks) - [Resources for students](#resources-for-students) - [Blogs](#blogs) - [Links](#links) - [Songs](#songs) ## Awesome Lists - [Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning) - [Awesome Deep Vision](https://github.com/kjw0612/awesome-deep-vision) - [Awesome Domain Adaptation](https://github.com/zhaoxin94/awesome-domain-adaptation) - [Awesome Object Detection](https://github.com/amusi/awesome-object-detection) - [Awesome 3D Machine Learning](https://github.com/timzhang642/3D-Machine-Learning) - [Awesome Action Recognition](https://github.com/jinwchoi/awesome-action-recognition) - [Awesome Scene Understanding](https://github.com/bertjiazheng/awesome-scene-understanding) - [Awesome Adversarial Machine Learning](https://github.com/yenchenlin/awesome-adversarial-machine-learning) - [Awesome Adversarial Deep Learning](https://github.com/chbrian/awesome-adversarial-examples-dl) - [Awesome Face](https://github.com/polarisZhao/awesome-face) - [Awesome Face Recognition](https://github.com/ChanChiChoi/awesome-Face_Recognition) - [Awesome Human Pose Estimation](https://github.com/wangzheallen/awesome-human-pose-estimation) - [Awesome medical imaging](https://github.com/fepegar/awesome-medical-imaging) - [Awesome Images](https://github.com/heyalexej/awesome-images) - [Awesome Graphics](https://github.com/ericjang/awesome-graphics) - [Awesome Neural Radiance Fields](https://github.com/yenchenlin/awesome-NeRF) - [Awesome Implicit Neural Representations](https://github.com/vsitzmann/awesome-implicit-representations) - [Awesome Neural Rendering](https://github.com/weihaox/awesome-neural-rendering) - [Awesome Public Datasets](https://github.com/awesomedata/awesome-public-datasets) - [Awesome Dataset Tools](https://github.com/jsbroks/awesome-dataset-tools) - [Awesome [Robotics](/@harrisonqian/awesome/wiki/hardware/robotics) Datasets](https://github.com/sunglok/awesome-robotics-datasets) - [Awesome Mobile Machine Learning](https://github.com/fritzlabs/Awesome-Mobile-Machine-Learning) - [Awesome Explainable AI](https://github.com/wangyongjie-ntu/Awesome-explainable-AI) - [Awesome Fairness in AI](https://github.com/datamllab/awesome-fairness-in-ai) - [Awesome [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) Interpretability](https://github.com/jphall663/awesome-machine-[learning](/@harrisonqian/awesome/wiki/programming-languages/learning)-interpretability) - [Awesome Production Machine Learning](https://github.com/EthicalML/awesome-production-machine-learning) - [Awesome Video Text Retrieval](https://github.com/danieljf24/awesome-video-text-retrieval) - [Awesome Image-to-Image Translation](https://github.com/weihaox/awesome-image-translation) - [Awesome Image Inpainting](https://github.com/1900zyh/Awesome-Image-Inpainting) - [Awesome Deep HDR](https://github.com/vinthony/awesome-deep-hdr) - [Awesome Video Generation](https://github.com/matthewvowels1/Awesome-Video-Generation) - [Awesome GAN applications](https://github.com/nashory/gans-[awesome](/@harrisonqian/awesome/wiki/miscellaneous/awesome)-applications) - [Awesome Generative Modeling](https://github.com/zhoubolei/awesome-generative-modeling) - [Awesome Image Classification](https://github.com/weiaicunzai/awesome-image-classification) - [Awesome Deep Learning](https://github.com/ChristosChristofidis/awesome-deep-learning) - [Awesome [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) in Biomedical(Healthcare) Imaging](https://github.com/XindiWu/Awesome-Machine-[Learning](/@harrisonqian/awesome/wiki/programming-languages/learning)-in-Biomedical-[Healthcare](/@harrisonqian/awesome/wiki/health-and-social-science/healthcare)-Imaging) - [Awesome [Deep Learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) for Tracking and Detection](https://github.com/abhineet123/Deep-[Learning](/@harrisonqian/awesome/wiki/programming-languages/learning)-for-Tracking-and-Detection) - [Awesome Human Pose Estimation](https://github.com/wangzheallen/awesome-human-pose-estimation) - [Awesome [Deep Learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) for Video Analysis](https://github.com/HuaizhengZhang/Awsome-Deep-[Learning](/@harrisonqian/awesome/wiki/programming-languages/learning)-for-Video-Analysis) - [Awesome Vision + Language](https://github.com/yuewang-cuhk/awesome-vision-language-pretraining-papers) - [Awesome Robotics](https://github.com/kiloreux/awesome-robotics) - [Awesome Visual Transformer](https://github.com/dk-liang/Awesome-Visual-Transformer) - [Awesome Embodied Vision](https://github.com/ChanganVR/awesome-embodied-vision) - [Awesome Anomaly Detection](https://github.com/hoya012/awesome-anomaly-detection) - [Awesome Makeup Transfer](https://github.com/thaoshibe/awesome-makeup-transfer) - [Awesome [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) with Label Noise](https://github.com/subeeshvasu/Awesome-Learning-with-Label-Noise) - [Awesome Deblurring](https://github.com/subeeshvasu/Awesome-Deblurring) - [Awsome Deep Geometry Learning](https://github.com/subeeshvasu/Awsome_Deep_Geometry_Learning) - [Awesome Image Distortion Correction](https://github.com/subeeshvasu/Awesome-Image-Distortion-Correction) - [Awesome Neuron Segmentation in EM Images](https://github.com/subeeshvasu/Awesome-Neuron-Segmentation-in-EM-Images) - [Awsome Delineation](https://github.com/subeeshvasu/Awsome_Delineation) - [Awesome ImageHarmonization](https://github.com/subeeshvasu/Awesome-ImageHarmonization) - [Awsome GAN Training](https://github.com/subeeshvasu/Awsome-GAN-Training) - [Awesome Document Understanding](https://github.com/tstanislawek/awesome-document-understanding) ## Books #### Computer Vision * [Computer Vision: Models, [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning), and Inference](http://www.computervisionmodels.com/) - Simon J. D. Prince 2012 * [Computer Vision: Theory and Application](http://szeliski.org/Book/) - Rick Szeliski 2010 * [Computer Vision: A Modern Approach (2nd edition)](http://www.amazon.com/Computer-Vision-Modern-Approach-2nd/dp/013608592X/ref=dp_ob_title_bk) - David Forsyth and Jean Ponce 2011 * [Multiple View Geometry in Computer Vision](http://www.robots.ox.ac.uk/~vgg/hzbook/) - Richard Hartley and Andrew Zisserman 2004 * [Computer Vision](http://www.amazon.com/Computer-Vision-Linda-G-Shapiro/dp/0130307963) - Linda G. Shapiro 2001 * [Vision Science: Photons to Phenomenology](http://www.amazon.com/Vision-Science-Phenomenology-Stephen-Palmer/dp/0262161834/) - Stephen E. Palmer 1999 * [Visual Object Recognition synthesis lecture](http://www.morganclaypool.com/doi/abs/10.2200/S00332ED1V01Y201103AIM011) - Kristen Grauman and Bastian Leibe 2011 * [Computer Vision for Visual Effects](http://cvfxbook.com/) - Richard J. Radke, 2012 * [High dynamic range imaging: acquisition, display, and image-based lighting](http://www.amazon.com/High-Dynamic-Range-Imaging-Second/dp/012374914X) - Reinhard, E., Heidrich, W., Debevec, P., Pattanaik, S., Ward, G., Myszkowski, K 2010 * [Numerical [Algorithms](/@harrisonqian/awesome/wiki/theory/algorithms): Methods for Computer Vision, [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning), and Graphics](https://people.csail.mit.edu/jsolomon/share/book/numerical_book.pdf) - Justin Solomon 2015 * [Image Processing and Analysis](https://www.amazon.com/Processing-Analysis-Activate-[Learning](/@harrisonqian/awesome/wiki/programming-languages/learning)-Engineering/dp/1285179528) - Stan Birchfield 2018 * [Computer Vision, From 3D Reconstruction to Recognition](http://web.stanford.edu/class/cs231a/) - Silvio Savarese 2018 #### OpenCV Programming * [Learning OpenCV: Computer Vision with the OpenCV Library](http://www.amazon.com/Learning-OpenCV-Computer-Vision-Library/dp/0596516134) - Gary Bradski and Adrian Kaehler * [Practical [Python](/@harrisonqian/awesome/wiki/programming-languages/python) and OpenCV](https://www.pyimagesearch.com/practical-python-opencv/) - Adrian Rosebrock * [OpenCV Essentials](http://www.amazon.com/OpenCV-Essentials-Oscar-Deniz-Suarez/dp/1783984244/ref=sr_1_1?s=books&ie=UTF8&qid=1424594237&sr=1-1&keywords=opencv+essentials#) - Oscar Deniz Suarez, Mª del Milagro Fernandez Carrobles, Noelia Vallez Enano, Gloria Bueno Garcia, Ismael Serrano Gracia #### Machine Learning * [Pattern Recognition and Machine Learning](http://research.microsoft.com/en-us/um/people/cmbishop/prml/index.htm) - Christopher M. Bishop 2007 * [Neural Networks for Pattern Recognition](http://www.engineering.upm.ro/master-ie/sacpi/mat_did/info068/docum/Neural%20Networks%20for%20Pattern%20Recognition.pdf) - Christopher M. Bishop 1995 * [Probabilistic Graphical Models: Principles and Techniques](http://pgm.stanford.edu/) - Daphne Koller and Nir Friedman 2009 * [Pattern Classification](http://www.amazon.com/Pattern-Classification-2nd-Richard-Duda/dp/0471056693) - Peter E. Hart, David G. Stork, and Richard O. Duda 2000 * [Machine Learning](http://www.amazon.com/Machine-[Learning](/@harrisonqian/awesome/wiki/programming-languages/learning)-Tom-M-Mitchell/dp/0070428077/) - Tom M. Mitchell 1997 * [Gaussian processes for machine learning](http://www.gaussianprocess.org/gpml/) - Carl Edward Rasmussen and Christopher K. I. Williams 2005 * [Learning From Data](https://work.caltech.edu/telecourse.html)- Yaser S. Abu-Mostafa, Malik Magdon-Ismail and Hsuan-Tien Lin 2012 * [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/) - Michael Nielsen 2014 * [Bayesian Reasoning and Machine Learning](http://www.cs.ucl.ac.uk/staff/d.barber/brml/) - David Barber, Cambridge University Press, 2012 #### Fundamentals * [Linear Algebra and Its Applications](http://www.amazon.com/Linear-Algebra-Its-Applications-4th/dp/0030105676/ref=sr_1_4?ie=UTF8&qid=1421433773&sr=8-4&keywords=Linear+Algebra+and+Its+Applications) - Gilbert Strang 1995 ## Courses #### Computer Vision * [EENG 512 / CSCI 512 - Computer Vision](http://inside.mines.edu/~whoff/courses/EENG512/) - William Hoff (Colorado School of Mines) * [Visual Object and Activity Recognition](https://sites.google.com/site/ucbcs29443/) - Alexei A. Efros and Trevor Darrell (UC Berkeley) * [Computer Vision](http://courses.cs.washington.edu/courses/cse455/12wi/) - Steve Seitz (University of Washington) * Visual Recognition [Spring 2016](http://vision.cs.utexas.edu/381V-spring2016/), [Fall 2016](http://vision.cs.utexas.edu/381V-fall2016/) - Kristen Grauman (UT Austin) * [Language and Vision](http://www.tamaraberg.com/teaching/Spring_15/) - Tamara Berg (UNC Chapel Hill) * [Convolutional Neural Networks for Visual Recognition](http://vision.stanford.edu/teaching/cs231n/) - Fei-Fei Li and Andrej Karpathy (Stanford University) * [Computer Vision](http://cs.nyu.edu/~fergus/teaching/vision/index.html) - Rob Fergus (NYU) * [Computer Vision](https://courses.engr.illinois.edu/cs543/sp2015/) - Derek Hoiem (UIUC) * [Computer Vision: Foundations and Applications](http://vision.stanford.edu/teaching/cs131_fall1415/index.html) - Kalanit Grill-Spector and Fei-Fei Li (Stanford University) * [High-Level Vision: Behaviors, Neurons and Computational Models](http://vision.stanford.edu/teaching/cs431_spring1314/) - Fei-Fei Li (Stanford University) * [Advances in Computer Vision](http://6.869.csail.mit.edu/fa15/) - Antonio Torralba and Bill Freeman (MIT) * [Computer Vision](http://www.vision.rwth-aachen.de/course/11/) - Bastian Leibe (RWTH Aachen University) * [Computer Vision 2](http://www.vision.rwth-aachen.de/course/9/) - Bastian Leibe (RWTH Aachen University) * [Computer Vision](http://klewel.com/conferences/epfl-computer-vision/) [Pascal](/@harrisonqian/awesome/wiki/programming-languages/pascal) Fua (EPFL): * [Computer Vision 1](http://cvlab-dresden.de/courses/computer-vision-1/) Carsten Rother (TU Dresden): * [Computer Vision 2](http://cvlab-dresden.de/courses/CV2/) Carsten Rother (TU Dresden): * [Multiple View Geometry](https://youtu.be/RDkwklFGMfo?list=PLTBdjV_4f-EJn6udZ34tht9EVIW7lbeo4) Daniel Cremers (TU Munich): #### Computational Photography * [Image Manipulation and Computational Photography](http://inst.eecs.berkeley.edu/~cs194-26/fa14/) - Alexei A. Efros (UC Berkeley) * [Computational Photography](http://graphics.cs.cmu.edu/courses/15-463/2012_fall/463.html) - Alexei A. Efros (CMU) * [Computational Photography](https://courses.engr.illinois.edu/cs498dh3/) - Derek Hoiem (UIUC) * [Computational Photography](http://cs.brown.edu/courses/csci1290/) - James Hays (Brown University) * [Digital & Computational Photography](http://stellar.mit.edu/S/course/6/sp12/6.815/) - Fredo Durand (MIT) * [Computational Camera and Photography](http://ocw.mit.edu/courses/media-arts-and-sciences/mas-531-computational-camera-and-photography-fall-2009/) - Ramesh Raskar (MIT Media Lab) * [Computational Photography](https://www.udacity.com/course/computational-photography--ud955) - Irfan Essa (Georgia Tech) * [Courses in Graphics](http://graphics.stanford.edu/courses/) - Stanford University * [Computational Photography](http://cs.nyu.edu/~fergus/teaching/comp_photo/index.html) - Rob Fergus (NYU) * [Introduction to Visual Computing](http://www.cs.toronto.edu/~kyros/courses/320/) - Kyros Kutulakos (University of Toronto) * [Computational Photography](http://www.cs.toronto.edu/~kyros/courses/2530/) - Kyros Kutulakos (University of Toronto) * [Computer Vision for Visual Effects](https://www.ecse.rpi.edu/~rjradke/cvfxcourse.html) - Rich Radke (Rensselaer Polytechnic Institute) * [Introduction to Image Processing](https://www.ecse.rpi.edu/~rjradke/improccourse.html) - Rich Radke (Rensselaer Polytechnic Institute) #### Machine Learning and Statistical Learning * [Machine Learning](https://www.coursera.org/learn/machine-learning) - Andrew Ng (Stanford University) * [Learning from Data](https://work.caltech.edu/telecourse.html) - Yaser S. Abu-Mostafa (Caltech) * [Statistical Learning](https://class.stanford.edu/courses/HumanitiesandScience/StatLearning/Winter2015/about) - Trevor Hastie and Rob Tibshirani (Stanford University) * [Statistical [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) Theory and Applications](http://www.mit.edu/~9.520/fall14/) - Tomaso Poggio, Lorenzo Rosasco, Carlo Ciliberto, Charlie Frogner, Georgios Evangelopoulos, Ben Deen (MIT) * [Statistical Learning](http://www.stat.rice.edu/~gallen/stat640.html) - Genevera Allen (Rice University) * [Practical Machine Learning](http://www.cs.berkeley.edu/~jordan/courses/294-fall09/) - Michael Jordan (UC Berkeley) * [Course on Information Theory, Pattern Recognition, and Neural Networks](http://videolectures.net/course_information_theory_pattern_recognition/) - David MacKay (University of Cambridge) * [Methods for Applied Statistics: Unsupervised Learning](http://web.stanford.edu/~lmackey/stats306b/) - Lester Mackey (Stanford) * [Machine Learning](http://www.robots.ox.ac.uk/~az/lectures/ml/index.html) - Andrew Zisserman (University of Oxford) * [Intro to Machine Learning](https://www.udacity.com/course/intro-to-machine-[learning](/@harrisonqian/awesome/wiki/programming-languages/learning)--ud120) - Sebastian Thrun (Stanford University) * [Machine Learning](https://www.udacity.com/course/machine-[learning](/@harrisonqian/awesome/wiki/programming-languages/learning)--ud262) - Charles Isbell, Michael Littman (Georgia Tech) * [(Convolutional) Neural Networks for Visual Recognition](https://cs231n.[github](/@harrisonqian/awesome/wiki/development-environment/github).io/) - Fei-Fei Li, Andrej Karphaty, Justin Johnson (Stanford University) * [Machine [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) for Computer Vision](https://youtu.be/QZmZFeZxEKI?list=PLTBdjV_4f-EIiongKlS9OKrBEp8QR47Wl) - Rudolph Triebel (TU Munich) #### Optimization * [Convex Optimization I](http://stanford.edu/class/ee364a/) - Stephen Boyd (Stanford University) * [Convex Optimization II](http://stanford.edu/class/ee364b/) - Stephen Boyd (Stanford University) * [Convex Optimization](https://class.stanford.edu/courses/Engineering/CVX101/Winter2014/about) - Stephen Boyd (Stanford University) * [Optimization at MIT](http://optimization.mit.edu/classes.php) - (MIT) * [Convex Optimization](http://www.stat.cmu.edu/~ryantibs/convexopt/) - Ryan Tibshirani (CMU) ## Papers #### Conference papers on the web * [CVPapers](http://www.cvpapers.com/) - Computer vision [papers](/@harrisonqian/awesome/wiki/computer-science/papers) on the web * [SIGGRAPH Paper on the web](http://kesen.realtimerendering.com/) - Graphics [papers](/@harrisonqian/awesome/wiki/computer-science/papers) on the web * [NIPS Proceedings](http://papers.nips.cc/) - NIPS [papers](/@harrisonqian/awesome/wiki/computer-science/papers) on the web * [Computer Vision Foundation open access](http://www.cv-foundation.org/openaccess/menu.py) * [Annotated Computer Vision Bibliography](http://iris.usc.edu/Vision-Notes/bibliography/contents.html) - Keith Price (USC) * [Calendar of Computer Image Analysis, Computer Vision Conferences](http://iris.usc.edu/Information/Iris-Conferences.html) - (USC) #### Survey Papers * [Visionbib Survey Paper List](http://surveys.visionbib.com/index.html) * [Foundations and Trends® in Computer Graphics and Vision](http://www.nowpublishers.com/CGV) * [Computer Vision: A Reference Guide](http://link.springer.com/book/10.1007/978-0-387-31439-6) ## Pre-trained Computer Vision Models * [List of Computer Vision models](https://github.com/shubham-shahh/Open-Source-Models) These models are trained on custom objects ## Tutorials and talks #### Computer Vision * [Computer Vision Talks](http://www.computervisiontalks.com/) - Lectures, keynotes, panel discussions on computer vision * [The Three R's of Computer Vision](https://www.youtube.com/watch?v=Mqg6eorYRIQ) - Jitendra Malik (UC Berkeley) 2013 * [Applications to Machine Vision](http://videolectures.net/epsrcws08_blake_amv/) - Andrew Blake (Microsoft Research) 2008 * [The Future of Image Search](http://videolectures.net/kdd08_malik_fis/?q=image) - Jitendra Malik (UC Berkeley) 2008 * [Should I do a PhD in Computer Vision?](https://www.youtube.com/watch?v=M17oGxh3Ny8) - Fatih Porikli (Australian National University) - [Graduate Summer School 2013: Computer Vision](http://www.ipam.ucla.edu/programs/summer-schools/graduate-summer-school-computer-vision/?tab=schedule) - IPAM, 2013 #### Recent Conference Talks - [CVPR 2015](http://www.pamitc.org/cvpr15/) - Jun 2015 - [ECCV 2014](http://videolectures.net/eccv2014_zurich/) - Sep 2014 - [CVPR 2014](http://techtalks.tv/cvpr-2014-oral-talks/) - Jun 2014 - [ICCV 2013](http://techtalks.tv/iccv2013/) - Dec 2013 - [ICML 2013](http://techtalks.tv/icml/2013/) - Jul 2013 - [CVPR 2013](http://techtalks.tv/cvpr2013/) - Jun 2013 - [ECCV 2012](http://videolectures.net/eccv2012_firenze/) - Oct 2012 - [ICML 2012](http://techtalks.tv/icml/2012/orals/) - Jun 2012 - [CVPR 2012](http://techtalks.tv/cvpr2012webcast/) - Jun 2012 #### 3D Computer Vision * [3D Computer Vision: Past, Present, and Future](https://www.youtube.com/watch?v=kyIzMr917Rc) - Steve Seitz (University of Washington) 2011 * [Reconstructing the World from Photos on the Internet](https://www.youtube.com/watch?v=04Kgg3QEXFI) - Steve Seitz (University of Washington) 2013 #### Internet Vision * [The Distributed Camera](http://www.technologyreview.com/video/426265/meet-2011-tr35-winner-noah-snavely/) - Noah Snavely (Cornell University) 2011 * [Planet-Scale Visual Understanding](https://www.youtube.com/watch?v=UHkCa9-Z1Ps) - Noah Snavely (Cornell University) 2014 * [A Trillion Photos](https://www.youtube.com/watch?v=6MWEfpKUfRc) - Steve Seitz (University of Washington) 2013 #### Computational Photography * [Reflections on Image-Based Modeling and Rendering](https://www.youtube.com/watch?v=j90_0Ndk7XM) - Richard Szeliski (Microsoft Research) 2013 * [Photographing Events over Time](https://www.youtube.com/watch?v=ZvPaHZZVPRk) - William T. Freeman (MIT) 2011 * [Old and New algorithm for Blind Deconvolution](http://videolectures.net/nipsworkshops2011_weiss_deconvolution/) - Yair Weiss (The Hebrew University of Jerusalem) 2011 * [A Tour of Modern "Image Processing"](http://videolectures.net/nipsworkshops2010_milanfar_tmi/) - Peyman Milanfar (UC Santa Cruz/Google) 2010 * [Topics in image and video processing](http://videolectures.net/mlss07_blake_tiivp/) Andrew Blake (Microsoft Research) 2007 * [Computational Photography](https://www.youtube.com/watch?v=HJVNI0mkmqk) - William T. Freeman (MIT) 2012 * [Revealing the Invisible](https://www.youtube.com/watch?v=_BWnIQY_X98) - Frédo Durand (MIT) 2012 * [Overview of Computer Vision and Visual Effects](https://www.youtube.com/watch?v=rE-hVtytT-I) - Rich Radke (Rensselaer Polytechnic Institute) 2014 #### Learning and Vision * [Where machine vision needs help from machine learning](http://videolectures.net/colt2011_freeman_help/?q=computer%20vision) - William T. Freeman (MIT) 2011 * [Learning in Computer Vision](http://videolectures.net/mlss08au_lucey_linv/) - Simon Lucey (CMU) 2008 * [Learning and Inference in Low-Level Vision](http://videolectures.net/nips09_weiss_lil/?q=computer%20vision) - Yair Weiss (The Hebrew University of Jerusalem) 2009 #### Object Recognition * [Object Recognition](http://research.microsoft.com/apps/video/dl.aspx?id=231358) - Larry Zitnick (Microsoft Research) * [Generative Models for Visual Objects and Object Recognition via Bayesian Inference](http://videolectures.net/mlas06_li_gmvoo/?q=Fei-Fei%20Li) - Fei-Fei Li (Stanford University) #### Graphical Models * [Graphical Models for Computer Vision](http://videolectures.net/uai2012_felzenszwalb_computer_vision/?q=computer%20vision) - Pedro Felzenszwalb (Brown University) 2012 * [Graphical Models](http://videolectures.net/mlss09uk_ghahramani_gm/) - Zoubin Ghahramani (University of Cambridge) 2009 * [Machine [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning), Probability and Graphical Models](http://videolectures.net/mlss06tw_roweis_mlpgm/) - Sam Roweis (NYU) 2006 * [Graphical Models and Applications](http://videolectures.net/mlss09us_weiss_gma/?q=Graphical%20Models) - Yair Weiss (The Hebrew University of Jerusalem) 2009 #### Machine Learning * [A Gentle Tutorial of the EM Algorithm](https://nikola-rt.ee.washington.edu/people/bulyko/papers/em.pdf) - Jeff A. Bilmes (UC Berkeley) 1998 * [Introduction To Bayesian Inference](http://videolectures.net/mlss09uk_bishop_ibi/) - Christopher Bishop (Microsoft Research) 2009 * [Support Vector Machines](http://videolectures.net/mlss06tw_lin_svm/) - Chih-Jen Lin (National Taiwan University) 2006 * [Bayesian or Frequentist, Which Are You? ](http://videolectures.net/mlss09uk_jordan_bfway/) - Michael I. Jordan (UC Berkeley) #### Optimization * [Optimization [Algorithms](/@harrisonqian/awesome/wiki/theory/algorithms) in Machine Learning](http://videolectures.net/nips2010_wright_oaml/) - Stephen J. Wright (University of Wisconsin-Madison) * [Convex Optimization](http://videolectures.net/mlss07_vandenberghe_copt/?q=convex%20optimization) - Lieven Vandenberghe (University of California, Los Angeles) * [Continuous Optimization in Computer Vision](https://www.youtube.com/watch?v=oZqoWozVDVg) - Andrew Fitzgibbon (Microsoft Research) * [Beyond stochastic gradient descent for large-scale machine learning](http://videolectures.net/sahd2014_bach_stochastic_gradient/) - Francis Bach (INRIA) * [Variational Methods for Computer Vision](https://www.youtube.com/playlist?list=PLTBdjV_4f-EJ7A2iIH5L5ztqqrWYjP2RI) - Daniel Cremers (Technische Universität München) ([lecture 18 missing from playlist](https://www.youtube.com/watch?v=GgcbVPNd3SI)) #### Deep Learning * [A tutorial on Deep Learning](http://videolectures.net/jul09_hinton_deeplearn/) - Geoffrey E. Hinton (University of Toronto) * [Deep Learning](http://videolectures.net/kdd2014_salakhutdinov_deep_learning/?q=Hidden%20Markov%20model#) - Ruslan Salakhutdinov (University of Toronto) * [Scaling up Deep Learning](http://videolectures.net/kdd2014_bengio_deep_learning/) - Yoshua Bengio (University of Montreal) * [ImageNet Classification with Deep Convolutional Neural Networks](http://videolectures.net/machine_krizhevsky_imagenet_classification/?q=deep%20learning) - Alex Krizhevsky (University of Toronto) * [The Unreasonable Effectivness Of Deep Learning](http://videolectures.net/sahd2014_lecun_deep_learning/) Yann LeCun (NYU/Facebook Research) 2014 * [Deep [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) for Computer Vision](https://www.youtube.com/watch?v=qgx57X0fBdA) - Rob Fergus (NYU/Facebook Research) * [High-dimensional [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) with deep network contractions](http://videolectures.net/sahd2014_mallat_dimensional_learning/) - Stéphane Mallat (Ecole Normale Superieure) * [Graduate Summer School 2012: [Deep Learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning), Feature Learning](http://www.ipam.ucla.edu/programs/summer-schools/graduate-summer-school-deep-[learning](/@harrisonqian/awesome/wiki/programming-languages/learning)-feature-learning/?tab=schedule) - IPAM, 2012 * [Workshop on [Big Data](/@harrisonqian/awesome/wiki/big-data/big-data) and Statistical Machine Learning](http://www.fields.utoronto.ca/programs/scientific/14-15/bigdata/machine/) * [Machine [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) Summer School](https://www.youtube.com/channel/UC3ywjSv5OsDiDAnOP8C1NiQ) - Reykjavik, Iceland 2014 * [Deep [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) Session 1](https://www.youtube.com/watch?v=JuimBuvEWBg) - Yoshua Bengio (Universtiy of Montreal) * [Deep [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) Session 2](https://www.youtube.com/watch?v=Fl-W7_z3w3o) - Yoshua Bengio (University of Montreal) * [Deep [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) Session 3](https://www.youtube.com/watch?v=_cohR7LAgWA) - Yoshua Bengio (University of Montreal) ## Software #### Annotation tools * [Comma Coloring](http://commacoloring.herokuapp.com/) * [Annotorious](https://annotorious.[github](/@harrisonqian/awesome/wiki/development-environment/github).io/) * [LabelME](http://labelme.csail.mit.edu/Release3.0/) * [gtmaker](https://github.com/sanko-shoko/gtmaker) #### External Resource Links * [Computer Vision Resources](https://sites.google.com/site/jbhuang0604/resources/vision) - Jia-Bin Huang (UIUC) * [Computer Vision Algorithm Implementations](http://www.cvpapers.com/rr.html) - CVPapers * [Source Code Collection for Reproducible Research](http://www.csee.wvu.edu/~xinl/reproducible_research.html) - Xin Li (West Virginia University) * [CMU Computer Vision Page](http://www.cs.cmu.edu/afs/cs/project/cil/ftp/html/v-source.html) #### General Purpose Computer Vision Library * [Open CV](http://opencv.org/) * [mexopencv](http://kyamagu.[github](/@harrisonqian/awesome/wiki/development-environment/github).io/mexopencv/) * [SimpleCV](http://simplecv.org/) * [Open source [Python](/@harrisonqian/awesome/wiki/programming-languages/python) module for computer vision](https://github.com/jesolem/PCV) * [ccv: A Modern Computer Vision Library](https://github.com/liuliu/ccv) * [VLFeat](http://www.vlfeat.org/) * [Matlab Computer Vision System Toolbox](http://www.mathworks.com/products/computer-vision/) * [Piotr's Computer Vision Matlab Toolbox](http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html) * [PCL: Point Cloud Library](http://pointclouds.org/) * [ImageUtilities](https://gitorious.org/imageutilities) #### Multiple-view Computer Vision * [MATLAB Functions for Multiple View Geometry](http://www.robots.ox.ac.uk/~vgg/hzbook/code/) * [Peter Kovesi's Matlab Functions for Computer Vision and Image Analysis](http://staffhome.ecm.uwa.edu.au/~00011811/Research/MatlabFns/index.html) * [OpenGV ](http://laurentkneip.[github](/@harrisonqian/awesome/wiki/development-environment/github).io/opengv/) - geometric computer vision [algorithms](/@harrisonqian/awesome/wiki/theory/algorithms) * [MinimalSolvers](http://cmp.felk.cvut.cz/mini/) - Minimal problems solver * [Multi-View Environment](http://www.gcc.tu-darmstadt.de/home/proj/mve/) * [Visual SFM](http://ccwu.me/vsfm/) * [Bundler SFM](http://www.cs.cornell.edu/~snavely/bundler/) * [openMVG: open Multiple View Geometry](http://imagine.enpc.fr/~moulonp/openMVG/) - Multiple View Geometry; Structure from Motion library & softwares * [Patch-based Multi-view Stereo V2](http://www.di.ens.fr/pmvs/) * [Clustering Views for Multi-view Stereo](http://www.di.ens.fr/cmvs/) * [Floating Scale Surface Reconstruction](http://www.gris.informatik.tu-darmstadt.de/projects/floating-scale-surface-recon/) * [Large-Scale Texturing of 3D Reconstructions](http://www.gcc.tu-darmstadt.de/home/proj/texrecon/) * [Awesome 3D reconstruction list](https://github.com/openMVG/awesome_3DReconstruction_list) #### Feature Detection and Extraction * [VLFeat](http://www.vlfeat.org/) * [SIFT](http://www.cs.ubc.ca/~lowe/keypoints/) * David G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, 60, 2 (2004), pp. 91-110. * [SIFT++](http://www.robots.ox.ac.uk/~vedaldi/code/siftpp.html) * [BRISK](http://www.asl.ethz.ch/people/lestefan/personal/BRISK) * Stefan Leutenegger, Margarita Chli and Roland Siegwart, "BRISK: Binary Robust Invariant Scalable Keypoints", ICCV 2011 * [SURF](http://www.vision.ee.ethz.ch/~surf/) * Herbert Bay, Andreas Ess, Tinne Tuytelaars, Luc Van Gool, "SURF: Speeded Up Robust Features", Computer Vision and Image Understanding (CVIU), Vol. 110, No. 3, pp. 346--359, 2008 * [FREAK](http://www.ivpe.com/freak.htm) * A. Alahi, R. Ortiz, and P. Vandergheynst, "FREAK: Fast Retina Keypoint", CVPR 2012 * [AKAZE](http://www.robesafe.com/personal/pablo.alcantarilla/kaze.html) * Pablo F. Alcantarilla, Adrien Bartoli and Andrew J. Davison, "KAZE Features", ECCV 2012 * [Local Binary Patterns](https://github.com/nourani/LBP) #### High Dynamic Range Imaging * [HDR_Toolbox](https://github.com/banterle/HDR_Toolbox) #### Semantic Segmentation * [List of Semantic Segmentation algorithms](http://www.it-caesar.com/list-of-contemporary-semantic-segmentation-datasets/) #### Low-level Vision ###### Stereo Vision * [Middlebury Stereo Vision](http://vision.middlebury.edu/stereo/) * [The KITTI Vision Benchmark Suite](http://www.cvlibs.net/datasets/kitti/eval_stereo_flow.php?benchmark=stero) * [LIBELAS: Library for Efficient Large-scale Stereo Matching](http://www.cvlibs.net/software/libelas/) * [Ground Truth Stixel Dataset](http://www.6d-vision.com/ground-truth-stixel-dataset) ###### Optical Flow * [Middlebury Optical Flow Evaluation](http://vision.middlebury.edu/flow/) * [MPI-Sintel Optical Flow Dataset and Evaluation](http://sintel.is.tue.mpg.de/) * [The KITTI Vision Benchmark Suite](http://www.cvlibs.net/datasets/kitti/eval_stereo_flow.php?benchmark=flow) * [HCI Challenge](http://hci.iwr.uni-heidelberg.de/Benchmarks/document/Challenging_Data_for_Stereo_and_Optical_Flow/) * [Coarse2Fine Optical Flow](http://people.csail.mit.edu/celiu/OpticalFlow/) - Ce Liu (MIT) * [Secrets of Optical Flow Estimation and Their Principles](http://cs.brown.edu/~dqsun/code/cvpr10_flow_code.zip) * [C++/MatLab Optical Flow by C. Liu (based on Brox et al. and Bruhn et al.)](http://people.csail.mit.edu/celiu/OpticalFlow/) * [Parallel Robust Optical Flow by Sánchez Pérez et al.](http://www.ctim.es/research_works/parallel_robust_optical_flow/) ###### Image Denoising BM3D, KSVD, ###### Super-resolution * [Multi-frame image super-resolution](http://www.robots.ox.ac.uk/~vgg/software/SR/) * Pickup, L. C. [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) in Multi-frame Image Super-resolution, PhD thesis 2008 * [Markov Random Fields for Super-Resolution](http://people.csail.mit.edu/billf/project%20pages/sresCode/Markov%20Random%20Fields%20for%20Super-Resolution.html) * W. T Freeman and C. Liu. Markov Random Fields for Super-resolution and Texture Synthesis. In A. Blake, P. Kohli, and C. Rother, eds., Advances in Markov Random Fields for Vision and Image Processing, Chapter 10. MIT Press, 2011 * [Sparse regression and natural image prior](https://people.mpi-inf.mpg.de/~kkim/supres/supres.htm) * K. I. Kim and Y. Kwon, "Single-image super-resolution using sparse regression and natural image prior", IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 32, no. 6, pp. 1127-1133, 2010. * [Single-Image Super Resolution via a Statistical Model](http://www.cs.technion.ac.il/~elad/Various/SingleImageSR_TIP14_Box.zip) * T. Peleg and M. Elad, A Statistical Prediction Model Based on Sparse Representations for Single Image Super-Resolution, IEEE Transactions on Image Processing, Vol. 23, No. 6, Pages 2569-2582, June 2014 * [Sparse Coding for Super-Resolution](http://www.cs.technion.ac.il/~elad/Various/Single_Image_SR.zip) * R. Zeyde, M. Elad, and M. Protter On Single Image Scale-Up using Sparse-Representations, Curves & Surfaces, Avignon-France, June 24-30, 2010 (appears also in Lecture-Notes-on-Computer-Science - LNCS). * [Patch-wise Sparse Recovery](http://www.ifp.illinois.edu/~jyang29/ScSR.htm) * Jianchao Yang, John Wright, Thomas Huang, and Yi Ma. Image super-resolution via sparse representation. IEEE Transactions on Image Processing (TIP), vol. 19, issue 11, 2010. * [Neighbor embedding](http://www.jdl.ac.cn/user/hchang/doc/code.rar) * H. Chang, D.Y. Yeung, Y. Xiong. Super-resolution through neighbor embedding. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol.1, pp.275-282, Washington, DC, USA, 27 June - 2 July 2004. * [Deformable Patches](https://sites.google.com/site/yuzhushome/single-image-super-resolution-using-deformable-patches) * Yu Zhu, Yanning Zhang and Alan Yuille, Single Image Super-resolution using Deformable Patches, CVPR 2014 * [SRCNN](http://mmlab.ie.cuhk.edu.hk/projects/SRCNN.html) * Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang, [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) a Deep Convolutional Network for Image Super-Resolution, in ECCV 2014 * [A+: Adjusted Anchored Neighborhood Regression](http://www.vision.ee.ethz.ch/~timofter/ACCV2014_ID820_SUPPLEMENTARY/index.html) * R. Timofte, V. De Smet, and L. Van Gool. A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution, ACCV 2014 * [Transformed Self-Exemplars](https://sites.google.com/site/jbhuang0604/publications/struct_sr) * Jia-Bin Huang, Abhishek Singh, and Narendra Ahuja, Single Image Super-Resolution using Transformed Self-Exemplars, IEEE Conference on Computer Vision and Pattern Recognition, 2015 ###### Image Deblurring Non-blind deconvolution * [Spatially variant non-blind deconvolution](http://homes.cs.washington.edu/~shanqi/work/spvdeconv/) * [Handling Outliers in Non-blind Image Deconvolution](http://cg.postech.ac.kr/research/deconv_outliers/) * [Hyper-Laplacian Priors](http://cs.nyu.edu/~dilip/research/fast-deconvolution/) * [From [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) Models of Natural Image Patches to Whole Image Restoration](http://people.csail.mit.edu/danielzoran/epllcode.zip) * [Deep Convolutional Neural Network for Image Deconvolution](http://lxu.me/projects/dcnn/) * [Neural Deconvolution](http://webdav.is.mpg.de/pixel/neural_deconvolution/) Blind deconvolution * [Removing Camera Shake From A Single Photograph](http://www.cs.nyu.edu/~fergus/research/deblur.html) * [High-quality motion deblurring from a single image](http://www.cse.cuhk.edu.hk/leojia/projects/motion_deblurring/) * [Two-Phase Kernel Estimation for Robust Motion Deblurring](http://www.cse.cuhk.edu.hk/leojia/projects/robust_deblur/) * [Blur kernel estimation using the radon transform](http://people.csail.mit.edu/taegsang/Documents/RadonDeblurringCode.zip) * [Fast motion deblurring](http://cg.postech.ac.kr/research/fast_motion_deblurring/) * [Blind Deconvolution Using a Normalized Sparsity Measure](http://cs.nyu.edu//~dilip/research/blind-deconvolution/) * [Blur-kernel estimation from spectral irregularities](http://www.cs.huji.ac.il/~raananf/projects/deblur/) * [Efficient marginal likelihood optimization in blind deconvolution](http://www.wisdom.weizmann.ac.il/~levina/papers/LevinEtalCVPR2011Code.zip) * [Unnatural L0 Sparse Representation for Natural Image Deblurring](http://www.cse.cuhk.edu.hk/leojia/projects/l0deblur/) * [Edge-based Blur Kernel Estimation Using Patch Priors](http://cs.brown.edu/~lbsun/deblur2013/deblur2013iccp.html) * [Blind Deblurring Using Internal Patch Recurrence](http://www.wisdom.weizmann.ac.il/~vision/BlindDeblur.html) Non-uniform Deblurring * [Non-uniform Deblurring for Shaken Images](http://www.di.ens.fr/willow/research/deblurring/) * [Single Image Deblurring Using Motion Density Functions](http://grail.cs.washington.edu/projects/mdf_deblurring/) * [Image Deblurring using Inertial Measurement Sensors](http://research.microsoft.com/en-us/um/redmond/groups/ivm/imudeblurring/) * [Fast Removal of Non-uniform Camera Shake](http://webdav.is.mpg.de/pixel/fast_removal_of_camera_shake/) ###### Image Completion * [GIMP Resynthesizer](http://registry.gimp.org/node/27986) * [Priority BP](http://lafarren.com/image-completer/) * [ImageMelding](http://www.ece.ucsb.edu/~psen/melding) * [PlanarStructureCompletion](https://sites.google.com/site/jbhuang0604/publications/struct_completion) ###### Image Retargeting * [RetargetMe](http://people.csail.mit.edu/mrub/retargetme/) ###### Alpha Matting * [Alpha Matting Evaluation](http://www.alphamatting.com/) * [Closed-form image matting](http://people.csail.mit.edu/alevin/matting.tar.gz) * [Spectral Matting](http://www.vision.huji.ac.il/SpectralMatting/) * [Learning-based Matting](http://www.mathworks.com/matlabcentral/fileexchange/31412-[learning](/@harrisonqian/awesome/wiki/programming-languages/learning)-based-digital-matting) * [Improving Image Matting using Comprehensive Sampling Sets](http://www.alphamatting.com/ImprovingMattingComprehensiveSamplingSets_CVPR2013.zip) ###### Image Pyramid * [The Steerable Pyramid](http://www.cns.nyu.edu/~eero/steerpyr/) * [CurveLab](http://www.curvelet.org/) ###### Edge-preserving image processing * [Fast Bilateral Filter](http://people.csail.mit.edu/sparis/bf/) * [O(1) Bilateral Filter](http://www.cs.cityu.edu.hk/~qiyang/publications/code/qx.cvpr09.ctbf.zip) * [Recursive Bilateral Filtering](http://www.cs.cityu.edu.hk/~qiyang/publications/eccv-12/) * [Rolling Guidance Filter](http://www.cse.cuhk.edu.hk/leojia/projects/rollguidance/) * [Relative Total Variation](http://www.cse.cuhk.edu.hk/leojia/projects/texturesep/index.html) * [L0 Gradient Optimization](http://www.cse.cuhk.edu.hk/leojia/projects/L0smoothing/index.html) * [Domain Transform](http://www.inf.ufrgs.br/~eslgastal/DomainTransform/) * [Adaptive Manifold](http://inf.ufrgs.br/~eslgastal/AdaptiveManifolds/) * [Guided image filtering](http://research.microsoft.com/en-us/um/people/kahe/eccv10/) #### Intrinsic Images * [Recovering Intrinsic Images with a global Sparsity Prior on Reflectance](http://people.tuebingen.mpg.de/mkiefel/projects/intrinsic/) * [Intrinsic Images by Clustering](http://giga.cps.unizar.es/~elenag/projects/EGSR2012_intrinsic/) #### Contour Detection and Image Segmentation * [Mean Shift Segmentation](http://coewww.rutgers.edu/riul/research/code/EDISON/) * [Graph-based Segmentation](http://cs.brown.edu/~pff/segment/) * [Normalized Cut](http://www.cis.upenn.edu/~jshi/software/) * [Grab Cut](http://grabcut.weebly.com/background--algorithm.html) * [Contour Detection and Image Segmentation](http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html) * [Structured Edge Detection](http://research.microsoft.com/en-us/downloads/389109f6-b4e8-404c-84bf-239f7cbf4e3d/) * [Pointwise Mutual Information](http://web.mit.edu/phillipi/pmi-boundaries/) * [SLIC Super-pixel](http://ivrl.epfl.ch/research/superpixels) * [QuickShift](http://www.vlfeat.org/overview/quickshift.html) * [TurboPixels](http://www.cs.toronto.edu/~babalex/research.html) * [Entropy Rate Superpixel](http://mingyuliu.net/) * [Contour Relaxed Superpixels](http://www.vsi.cs.uni-frankfurt.de/research/current-projects/research/superpixel-segmentation/) * [SEEDS](http://www.mvdblive.org/seeds/) * [SEEDS Revised](https://github.com/davidstutz/seeds-revised) * [Multiscale Combinatorial Grouping](http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/mcg/) * [Fast Edge Detection Using Structured Forests](https://github.com/pdollar/edges) #### Interactive Image Segmentation * [Random Walker](http://cns.bu.edu/~lgrady/software.html) * [Geodesic Segmentation](http://www.tc.umn.edu/~baixx015/) * [Lazy Snapping](http://research.microsoft.com/apps/pubs/default.aspx?id=69040) * [Power Watershed](http://powerwatershed.sourceforge.net/) * [Geodesic Graph Cut](http://www.adobe.com/technology/people/san-jose/brian-price.html) * [Segmentation by Transduction](http://www.cs.cmu.edu/~olivierd/) #### Video Segmentation * [Video Segmentation with Superpixels](http://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/image-and-video-segmentation/video-segmentation-with-superpixels/) * [Efficient hierarchical graph-based video segmentation](http://www.cc.gatech.edu/cpl/projects/videosegmentation/) * [Object segmentation in video](http://lmb.informatik.uni-freiburg.de/Publications/2011/OB11/) * [Streaming hierarchical video segmentation](http://www.cse.buffalo.edu/~jcorso/r/supervoxels/) #### Camera calibration * [Camera Calibration Toolbox for Matlab](http://www.vision.caltech.edu/bouguetj/calib_doc/) * [Camera calibration With OpenCV](http://docs.opencv.org/trunk/doc/tutorials/calib3d/camera_calibration/camera_calibration.html#) * [Multiple Camera Calibration Toolbox](https://sites.google.com/site/prclibo/toolbox) #### Simultaneous localization and mapping ###### SLAM community: * [openSLAM](https://www.openslam.org/) * [Kitti Odometry: benchmark for outdoor visual odometry (codes may be available)](http://www.cvlibs.net/datasets/kitti/eval_odometry.php) ###### Tracking/Odometry: * [LIBVISO2: C++ Library for Visual Odometry 2](http://www.cvlibs.net/software/libviso/) * [PTAM: Parallel tracking and mapping](http://www.robots.ox.ac.uk/~gk/PTAM/) * [KFusion: Implementation of KinectFusion](https://github.com/GerhardR/kfusion) * [kinfu_remake: Lightweight, reworked and optimized version of Kinfu.](https://github.com/Nerei/kinfu_remake) * [LVR-KinFu: kinfu_remake based Large Scale KinectFusion with online reconstruction](http://las-vegas.uni-osnabrueck.de/related-projects/lvr-kinfu/) * [InfiniTAM: Implementation of multi-platform large-scale depth tracking and fusion](http://www.robots.ox.ac.uk/~victor/infinitam/) * [VoxelHashing: Large-scale KinectFusion](https://github.com/nachtmar/VoxelHashing) * [SLAMBench: Multiple-implementation of KinectFusion](http://apt.cs.manchester.ac.uk/projects/PAMELA/tools/SLAMBench/) * [SVO: Semi-direct visual odometry](https://github.com/uzh-rpg/rpg_svo) * [DVO: dense visual odometry](https://github.com/tum-vision/dvo_slam) --- *truncated — [full list on GitHub](https://github.com/jbhuang0604/awesome-computer-vision)*