Computer Vision

repo: jbhuang0604/awesome-computer-vision
category: Computer Science related: Deep Learning · Machine Learning · Python · Generative Ai

Awesome Computer Vision:

A curated list of awesome computer vision resources, inspired by awesome-php.

For a list people in computer vision listed with their academic genealogy, please visit here

Contributing

Please feel free to send me pull requests or email ([email protected]) to add links.

Table of Contents

• Awesome Lists
• Books
• Courses
• Papers
• Software
• Datasets
• Pre-trained Computer Vision Models
• Tutorials and Talks
• Resources for students
• Blogs
• Links
• Songs

Awesome Lists

• Awesome Machine Learning
• Awesome Deep Vision
• Awesome Domain Adaptation
• Awesome Object Detection
• Awesome 3D Machine Learning
• Awesome Action Recognition
• Awesome Scene Understanding
• Awesome Adversarial Machine Learning
• Awesome Adversarial Deep Learning
• Awesome Face
• Awesome Face Recognition
• Awesome Human Pose Estimation
• Awesome medical imaging
• Awesome Images
• Awesome Graphics
• Awesome Neural Radiance Fields
• Awesome Implicit Neural Representations
• Awesome Neural Rendering
• Awesome Public Datasets
• Awesome Dataset Tools
• [Awesome Robotics Datasets](https://github.com/sunglok/awesome-robotics-datasets)
• Awesome Mobile Machine Learning
• Awesome Explainable AI
• Awesome Fairness in AI
• [Awesome Machine Learning Interpretability](https://github.com/jphall663/awesome-machine-learning-interpretability)
• Awesome Production Machine Learning
• Awesome Video Text Retrieval
• Awesome Image-to-Image Translation
• Awesome Image Inpainting
• Awesome Deep HDR
• Awesome Video Generation
• Awesome GAN applications
• Awesome Generative Modeling
• Awesome Image Classification
• Awesome Deep Learning
• [Awesome Machine Learning in Biomedical(Healthcare) Imaging](https://github.com/XindiWu/Awesome-Machine-Learning-in-Biomedical-Healthcare-Imaging)
• [Awesome Deep Learning for Tracking and Detection](https://github.com/abhineet123/Deep-Learning-for-Tracking-and-Detection)
• Awesome Human Pose Estimation
• [Awesome Deep Learning for Video Analysis](https://github.com/HuaizhengZhang/Awsome-Deep-Learning-for-Video-Analysis)
• Awesome Vision + Language
• Awesome Robotics
• Awesome Visual Transformer
• Awesome Embodied Vision
• Awesome Anomaly Detection
• Awesome Makeup Transfer
• [Awesome Learning with Label Noise](https://github.com/subeeshvasu/Awesome-Learning-with-Label-Noise)
• Awesome Deblurring
• Awsome Deep Geometry Learning
• Awesome Image Distortion Correction
• Awesome Neuron Segmentation in EM Images
• Awsome Delineation
• Awesome ImageHarmonization
• Awsome GAN Training
• Awesome Document Understanding

Books

Computer Vision

• [Computer Vision: Models, Learning, and Inference](http://www.computervisionmodels.com/) - Simon J. D. Prince 2012
• Computer Vision: Theory and Application - Rick Szeliski 2010
• Computer Vision: A Modern Approach (2nd edition) - David Forsyth and Jean Ponce 2011
• Multiple View Geometry in Computer Vision - Richard Hartley and Andrew Zisserman 2004
• Computer Vision - Linda G. Shapiro 2001
• Vision Science: Photons to Phenomenology - Stephen E. Palmer 1999
• Visual Object Recognition synthesis lecture - Kristen Grauman and Bastian Leibe 2011
• Computer Vision for Visual Effects - Richard J. Radke, 2012
• High dynamic range imaging: acquisition, display, and image-based lighting - Reinhard, E., Heidrich, W., Debevec, P., Pattanaik, S., Ward, G., Myszkowski, K 2010
• [Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics](https://people.csail.mit.edu/jsolomon/share/book/numerical_book.pdf) - Justin Solomon 2015
• Image Processing and Analysis - Stan Birchfield 2018
• Computer Vision, From 3D Reconstruction to Recognition - Silvio Savarese 2018

OpenCV Programming

• Learning OpenCV: Computer Vision with the OpenCV Library - Gary Bradski and Adrian Kaehler
• [Practical Python and OpenCV](https://www.pyimagesearch.com/practical-python-opencv/) - Adrian Rosebrock
• 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 - Christopher M. Bishop 2007
• Neural Networks for Pattern Recognition - Christopher M. Bishop 1995
• Probabilistic Graphical Models: Principles and Techniques - Daphne Koller and Nir Friedman 2009
• Pattern Classification - Peter E. Hart, David G. Stork, and Richard O. Duda 2000
• Machine Learning - Tom M. Mitchell 1997
• Gaussian processes for machine learning - Carl Edward Rasmussen and Christopher K. I. Williams 2005
• Learning From Data- Yaser S. Abu-Mostafa, Malik Magdon-Ismail and Hsuan-Tien Lin 2012
• Neural Networks and Deep Learning - Michael Nielsen 2014
• Bayesian Reasoning and Machine Learning - David Barber, Cambridge University Press, 2012

Fundamentals

• Linear Algebra and Its Applications - Gilbert Strang 1995

Courses

Computer Vision

• EENG 512 / CSCI 512 - Computer Vision - William Hoff (Colorado School of Mines)
• Visual Object and Activity Recognition - Alexei A. Efros and Trevor Darrell (UC Berkeley)
• Computer Vision - Steve Seitz (University of Washington)
• Visual Recognition Spring 2016, Fall 2016 - Kristen Grauman (UT Austin)
• Language and Vision - Tamara Berg (UNC Chapel Hill)
• Convolutional Neural Networks for Visual Recognition - Fei-Fei Li and Andrej Karpathy (Stanford University)
• Computer Vision - Rob Fergus (NYU)
• Computer Vision - Derek Hoiem (UIUC)
• Computer Vision: Foundations and Applications - Kalanit Grill-Spector and Fei-Fei Li (Stanford University)
• High-Level Vision: Behaviors, Neurons and Computational Models - Fei-Fei Li (Stanford University)
• Advances in Computer Vision - Antonio Torralba and Bill Freeman (MIT)
• Computer Vision - Bastian Leibe (RWTH Aachen University)
• Computer Vision 2 - Bastian Leibe (RWTH Aachen University)
• Computer Vision Pascal Fua (EPFL):
• Computer Vision 1 Carsten Rother (TU Dresden):
• Computer Vision 2 Carsten Rother (TU Dresden):
• Multiple View Geometry Daniel Cremers (TU Munich):

Computational Photography

• Image Manipulation and Computational Photography - Alexei A. Efros (UC Berkeley)
• Computational Photography - Alexei A. Efros (CMU)
• Computational Photography - Derek Hoiem (UIUC)
• Computational Photography - James Hays (Brown University)
• Digital & Computational Photography - Fredo Durand (MIT)
• Computational Camera and Photography - Ramesh Raskar (MIT Media Lab)
• Computational Photography - Irfan Essa (Georgia Tech)
• Courses in Graphics - Stanford University
• Computational Photography - Rob Fergus (NYU)
• Introduction to Visual Computing - Kyros Kutulakos (University of Toronto)
• Computational Photography - Kyros Kutulakos (University of Toronto)
• Computer Vision for Visual Effects - Rich Radke (Rensselaer Polytechnic Institute)
• Introduction to Image Processing - Rich Radke (Rensselaer Polytechnic Institute)

Machine Learning and Statistical Learning

• Machine Learning - Andrew Ng (Stanford University)
• Learning from Data - Yaser S. Abu-Mostafa (Caltech)
• Statistical Learning - Trevor Hastie and Rob Tibshirani (Stanford University)
• [Statistical 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 - Genevera Allen (Rice University)
• Practical Machine Learning - Michael Jordan (UC Berkeley)
• Course on Information Theory, Pattern Recognition, and Neural Networks - David MacKay (University of Cambridge)
• Methods for Applied Statistics: Unsupervised Learning - Lester Mackey (Stanford)
• Machine Learning - Andrew Zisserman (University of Oxford)
• Intro to Machine Learning - Sebastian Thrun (Stanford University)
• Machine Learning - Charles Isbell, Michael Littman (Georgia Tech)
• (Convolutional) Neural Networks for Visual Recognition - Fei-Fei Li, Andrej Karphaty, Justin Johnson (Stanford University)
• [Machine Learning for Computer Vision](https://youtu.be/QZmZFeZxEKI?list=PLTBdjV_4f-EIiongKlS9OKrBEp8QR47Wl) - Rudolph Triebel (TU Munich)

Optimization

• Convex Optimization I - Stephen Boyd (Stanford University)
• Convex Optimization II - Stephen Boyd (Stanford University)
• Convex Optimization - Stephen Boyd (Stanford University)
• Optimization at MIT - (MIT)
• Convex Optimization - Ryan Tibshirani (CMU)

Papers

Conference papers on the web

• CVPapers - Computer vision papers on the web
• SIGGRAPH Paper on the web - Graphics papers on the web
• NIPS Proceedings - NIPS papers on the web
• Computer Vision Foundation open access
• Annotated Computer Vision Bibliography - Keith Price (USC)
• Calendar of Computer Image Analysis, Computer Vision Conferences - (USC)

Survey Papers

• Visionbib Survey Paper List
• Foundations and Trends® in Computer Graphics and Vision
• Computer Vision: A Reference Guide

Pre-trained Computer Vision Models

• List of Computer Vision models These models are trained on custom objects

Tutorials and talks

Computer Vision

• Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
• The Three R's of Computer Vision - Jitendra Malik (UC Berkeley) 2013
• Applications to Machine Vision - Andrew Blake (Microsoft Research) 2008
• The Future of Image Search - Jitendra Malik (UC Berkeley) 2008
• Should I do a PhD in Computer Vision? - Fatih Porikli (Australian National University)

• Graduate Summer School 2013: Computer Vision - IPAM, 2013

Recent Conference Talks

• CVPR 2015 - Jun 2015
• ECCV 2014 - Sep 2014
• CVPR 2014 - Jun 2014
• ICCV 2013 - Dec 2013
• ICML 2013 - Jul 2013
• CVPR 2013 - Jun 2013
• ECCV 2012 - Oct 2012
• ICML 2012 - Jun 2012
• CVPR 2012 - Jun 2012

3D Computer Vision

• 3D Computer Vision: Past, Present, and Future - Steve Seitz (University of Washington) 2011
• Reconstructing the World from Photos on the Internet - Steve Seitz (University of Washington) 2013

Internet Vision

• The Distributed Camera - Noah Snavely (Cornell University) 2011
• Planet-Scale Visual Understanding - Noah Snavely (Cornell University) 2014
• A Trillion Photos - Steve Seitz (University of Washington) 2013

Computational Photography

• Reflections on Image-Based Modeling and Rendering - Richard Szeliski (Microsoft Research) 2013
• Photographing Events over Time - William T. Freeman (MIT) 2011
• Old and New algorithm for Blind Deconvolution - Yair Weiss (The Hebrew University of Jerusalem) 2011
• A Tour of Modern "Image Processing" - Peyman Milanfar (UC Santa Cruz/Google) 2010
• Topics in image and video processing Andrew Blake (Microsoft Research) 2007
• Computational Photography - William T. Freeman (MIT) 2012
• Revealing the Invisible - Frédo Durand (MIT) 2012
• Overview of Computer Vision and Visual Effects - Rich Radke (Rensselaer Polytechnic Institute) 2014

Learning and Vision

• Where machine vision needs help from machine learning - William T. Freeman (MIT) 2011
• Learning in Computer Vision - Simon Lucey (CMU) 2008
• Learning and Inference in Low-Level Vision - Yair Weiss (The Hebrew University of Jerusalem) 2009

Object Recognition

• Object Recognition - Larry Zitnick (Microsoft Research)
• Generative Models for Visual Objects and Object Recognition via Bayesian Inference - Fei-Fei Li (Stanford University)

Graphical Models

• Graphical Models for Computer Vision - Pedro Felzenszwalb (Brown University) 2012
• Graphical Models - Zoubin Ghahramani (University of Cambridge) 2009
• [Machine Learning, Probability and Graphical Models](http://videolectures.net/mlss06tw_roweis_mlpgm/) - Sam Roweis (NYU) 2006
• Graphical Models and Applications - Yair Weiss (The Hebrew University of Jerusalem) 2009

Machine Learning

• A Gentle Tutorial of the EM Algorithm - Jeff A. Bilmes (UC Berkeley) 1998
• Introduction To Bayesian Inference - Christopher Bishop (Microsoft Research) 2009
• Support Vector Machines - Chih-Jen Lin (National Taiwan University) 2006
• Bayesian or Frequentist, Which Are You? - Michael I. Jordan (UC Berkeley)

Optimization

• [Optimization Algorithms in Machine Learning](http://videolectures.net/nips2010_wright_oaml/) - Stephen J. Wright (University of Wisconsin-Madison)
• Convex Optimization - Lieven Vandenberghe (University of California, Los Angeles)
• Continuous Optimization in Computer Vision - Andrew Fitzgibbon (Microsoft Research)
• Beyond stochastic gradient descent for large-scale machine learning - Francis Bach (INRIA)
• Variational Methods for Computer Vision - Daniel Cremers (Technische Universität München) (lecture 18 missing from playlist)

Deep Learning

• A tutorial on Deep Learning - Geoffrey E. Hinton (University of Toronto)
• Deep Learning - Ruslan Salakhutdinov (University of Toronto)
• Scaling up Deep Learning - Yoshua Bengio (University of Montreal)
• ImageNet Classification with Deep Convolutional Neural Networks - Alex Krizhevsky (University of Toronto)
• The Unreasonable Effectivness Of Deep Learning Yann LeCun (NYU/Facebook Research) 2014
• [Deep Learning for Computer Vision](https://www.youtube.com/watch?v=qgx57X0fBdA) - Rob Fergus (NYU/Facebook Research)
• [High-dimensional learning with deep network contractions](http://videolectures.net/sahd2014_mallat_dimensional_learning/) - Stéphane Mallat (Ecole Normale Superieure)
• [Graduate Summer School 2012: Deep Learning, Feature Learning](http://www.ipam.ucla.edu/programs/summer-schools/graduate-summer-school-deep-learning-feature-learning/?tab=schedule) - IPAM, 2012
• [Workshop on Big Data and Statistical Machine Learning](http://www.fields.utoronto.ca/programs/scientific/14-15/bigdata/machine/)
• [Machine Learning Summer School](https://www.youtube.com/channel/UC3ywjSv5OsDiDAnOP8C1NiQ) - Reykjavik, Iceland 2014
  • [Deep Learning Session 1](https://www.youtube.com/watch?v=JuimBuvEWBg) - Yoshua Bengio (Universtiy of Montreal)
  • [Deep Learning Session 2](https://www.youtube.com/watch?v=Fl-W7_z3w3o) - Yoshua Bengio (University of Montreal)
  • [Deep Learning Session 3](https://www.youtube.com/watch?v=_cohR7LAgWA) - Yoshua Bengio (University of Montreal)

Software

Annotation tools

• Comma Coloring
• Annotorious
• LabelME
• gtmaker

External Resource Links

• Computer Vision Resources - Jia-Bin Huang (UIUC)
• Computer Vision Algorithm Implementations - CVPapers
• Source Code Collection for Reproducible Research - Xin Li (West Virginia University)
• CMU Computer Vision Page

General Purpose Computer Vision Library

• Open CV
• mexopencv
• SimpleCV
• [Open source Python module for computer vision](https://github.com/jesolem/PCV)
• ccv: A Modern Computer Vision Library
• VLFeat
• Matlab Computer Vision System Toolbox
• Piotr's Computer Vision Matlab Toolbox
• PCL: Point Cloud Library
• ImageUtilities

Multiple-view Computer Vision

• MATLAB Functions for Multiple View Geometry
• Peter Kovesi's Matlab Functions for Computer Vision and Image Analysis
• OpenGV - geometric computer vision algorithms
• MinimalSolvers - Minimal problems solver
• Multi-View Environment
• Visual SFM
• Bundler SFM
• openMVG: open Multiple View Geometry - Multiple View Geometry; Structure from Motion library & softwares
• Patch-based Multi-view Stereo V2
• Clustering Views for Multi-view Stereo
• Floating Scale Surface Reconstruction
• Large-Scale Texturing of 3D Reconstructions
• Awesome 3D reconstruction list

Feature Detection and Extraction

• VLFeat
• SIFT
  • David G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, 60, 2 (2004), pp. 91-110.
• SIFT++
• BRISK
  • Stefan Leutenegger, Margarita Chli and Roland Siegwart, "BRISK: Binary Robust Invariant Scalable Keypoints", ICCV 2011
• 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
  • A. Alahi, R. Ortiz, and P. Vandergheynst, "FREAK: Fast Retina Keypoint", CVPR 2012
• AKAZE
  • Pablo F. Alcantarilla, Adrien Bartoli and Andrew J. Davison, "KAZE Features", ECCV 2012
• Local Binary Patterns

High Dynamic Range Imaging

• HDR_Toolbox

Semantic Segmentation

• List of Semantic Segmentation algorithms

Low-level Vision

Stereo Vision

• Middlebury Stereo Vision
• The KITTI Vision Benchmark Suite
• LIBELAS: Library for Efficient Large-scale Stereo Matching
• Ground Truth Stixel Dataset

Optical Flow

• Middlebury Optical Flow Evaluation
• MPI-Sintel Optical Flow Dataset and Evaluation
• The KITTI Vision Benchmark Suite
• HCI Challenge
• Coarse2Fine Optical Flow - Ce Liu (MIT)
• Secrets of Optical Flow Estimation and Their Principles
• C++/MatLab Optical Flow by C. Liu (based on Brox et al. and Bruhn et al.)
• Parallel Robust Optical Flow by Sánchez Pérez et al.

Image Denoising

BM3D, KSVD,

Super-resolution

• Multi-frame image super-resolution
  • Pickup, L. C. Machine Learning in Multi-frame Image Super-resolution, PhD thesis 2008
• Markov Random Fields for Super-Resolution
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Yu Zhu, Yanning Zhang and Alan Yuille, Single Image Super-resolution using Deformable Patches, CVPR 2014
• SRCNN
  • Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang, Learning a Deep Convolutional Network for Image Super-Resolution, in ECCV 2014
• A+: Adjusted Anchored Neighborhood Regression
  • R. Timofte, V. De Smet, and L. Van Gool. A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution, ACCV 2014
• Transformed Self-Exemplars
  • 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
• Handling Outliers in Non-blind Image Deconvolution
• Hyper-Laplacian Priors
• [From 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
• Neural Deconvolution

Blind deconvolution

• Removing Camera Shake From A Single Photograph
• High-quality motion deblurring from a single image
• Two-Phase Kernel Estimation for Robust Motion Deblurring
• Blur kernel estimation using the radon transform
• Fast motion deblurring
• Blind Deconvolution Using a Normalized Sparsity Measure
• Blur-kernel estimation from spectral irregularities
• Efficient marginal likelihood optimization in blind deconvolution
• Unnatural L0 Sparse Representation for Natural Image Deblurring
• Edge-based Blur Kernel Estimation Using Patch Priors
• Blind Deblurring Using Internal Patch Recurrence

Non-uniform Deblurring

• Non-uniform Deblurring for Shaken Images
• Single Image Deblurring Using Motion Density Functions
• Image Deblurring using Inertial Measurement Sensors
• Fast Removal of Non-uniform Camera Shake

Image Completion

• GIMP Resynthesizer
• Priority BP
• ImageMelding
• PlanarStructureCompletion

Image Retargeting

• RetargetMe

Alpha Matting

• Alpha Matting Evaluation
• Closed-form image matting
• Spectral Matting
• Learning-based Matting
• Improving Image Matting using Comprehensive Sampling Sets

Image Pyramid

• The Steerable Pyramid
• CurveLab

Edge-preserving image processing

• Fast Bilateral Filter
• O(1) Bilateral Filter
• Recursive Bilateral Filtering
• Rolling Guidance Filter
• Relative Total Variation
• L0 Gradient Optimization
• Domain Transform
• Adaptive Manifold
• Guided image filtering

Intrinsic Images

• Recovering Intrinsic Images with a global Sparsity Prior on Reflectance
• Intrinsic Images by Clustering

Contour Detection and Image Segmentation

• Mean Shift Segmentation
• Graph-based Segmentation
• Normalized Cut
• Grab Cut
• Contour Detection and Image Segmentation
• Structured Edge Detection
• Pointwise Mutual Information
• SLIC Super-pixel
• QuickShift
• TurboPixels
• Entropy Rate Superpixel
• Contour Relaxed Superpixels
• SEEDS
• SEEDS Revised
• Multiscale Combinatorial Grouping
• Fast Edge Detection Using Structured Forests

Interactive Image Segmentation

• Random Walker
• Geodesic Segmentation
• Lazy Snapping
• Power Watershed
• Geodesic Graph Cut
• Segmentation by Transduction

Video Segmentation

• Video Segmentation with Superpixels
• Efficient hierarchical graph-based video segmentation
• Object segmentation in video
• Streaming hierarchical video segmentation

Camera calibration

• Camera Calibration Toolbox for Matlab
• Camera calibration With OpenCV
• Multiple Camera Calibration Toolbox

Simultaneous localization and mapping

SLAM community:

• openSLAM
• Kitti Odometry: benchmark for outdoor visual odometry (codes may be available)

Tracking/Odometry:

• LIBVISO2: C++ Library for Visual Odometry 2
• PTAM: Parallel tracking and mapping
• KFusion: Implementation of KinectFusion
• kinfu_remake: Lightweight, reworked and optimized version of Kinfu.
• LVR-KinFu: kinfu_remake based Large Scale KinectFusion with online reconstruction
• InfiniTAM: Implementation of multi-platform large-scale depth tracking and fusion
• VoxelHashing: Large-scale KinectFusion
• SLAMBench: Multiple-implementation of KinectFusion
• SVO: Semi-direct visual odometry
• DVO: dense visual odometry

truncated — full list on GitHub