Digital Image Correlation & Digital Volume Correlation Library
OpenCorr is an open source C++ library for research and development of 2D, 3D/stereo, and volumetric digital image correlation (DIC). It aims to provide a developer-friendly, lightweight, and efficient kit to the users who are willing to study the state-of-the-art algorithms of DIC and DVC (digital volume correlation), or to create DIC and DVC programs for their specific applications.
Comments and suggestions are most welcome. You may reach us via
Users can also access the information of OpenCorr via website opencorr.org .
2021.04.23, OpenCorr is released to public.
2021.04.30, Modify structure of DIC module and stereovision module.
2021.05.08, A brief instruction of framework is released.
2021.05.17, Improve the adaptability for Linux and release a cool title figure.
2021.06.12, Release an example to demonstrate the calculation of strains, update the documentation.
2021.08.14, Release the GPU accelerated module of ICGN algorithm and an example, instruction can be found in Instructions (5. GPU acceleration).
2021.11.03, Release an example to implement stereo DIC (3D DIC), thoroughly improve the related modules and documentation.
2021.11.16, Implement the calculation of 2D and 3D strains for surface measurement.
2022.04.27, A major update, including (i) introduction of nanoflann to speed up the searching for nearest neighbors in Feature Affine method and strain calculation; (ii) update of the third party libraries (Eigen and OpenCV) to the latest stable version; (iii) regularization of the codes.
2022.05.03, Estimation of parallax for epipolar constraint aided matching, and an example of stereo matching and reconstruction combining the methods using SIFT feature and epipolar constraint.
2022.06.23, Release DVC module, which includes 3D FFTCC and 3D ICGN algorithms. The related modules are expanded accordingly.
2022.10.13, Fix the VRAM leak issue of GPU accelerated ICGN module.
2022.10.21, Fix the conflict of calling NearestNeighbor instance by multiple threads in modules FeatureAffine and Strain.
2022.12.23, Release of OpenCorr 1.0. Modules Feature and FeatureAffine are upgraded by introducing calsses SIFT3D and FeatureAffine3D, respectively. The codes, examples, and documents are thoroughly updated.
2023.01.13, A regular update, including (i) adding a module of Newton-Raphson algorithm (NR) for 2D DIC; (ii) giving an example of self-adaptive DIC, which can dynamically optimize the size and shape of subset at each POI; (iii) fixing a potential bug in module Interpolation; (iv) updating the codes and documents.
2023.01.18, Add description of examples.
2023.03.06, A research paper titled "OpenCorr: An open source library for research and development of digital image correlation" is published in Optics and Lasers in Engineering.
2024.02.07, Major update of ICGN module: (i) GPU accelerated ICGN (ICGNGPU) has been completely reconstructed, adding the function of DVC. The calling method of ICGNGPU is now similar to the CPU version. Most of redundant data conversion and transfer are eliminated. Two examples are added to the folder /examples to demonstrate the use of ICGNGPU. (ii) CPU version is modified to improve the efficiency.
OpenCorr demonstrates our exploration of DIC and DVC methods in recent years, which got continuous financial support from National Natural Science Foundation of China. I would like to give my special thanks to two collaborators for their enthusiastic support: Professor QIAN Kemao (Nanyang Technological University) and Professor DONG Shoubin (South China University of Technology).
Users may refer to our papers for more information about the detailed principles and implementations of the algorithms in OpenCorr. If you feel OpenCorr helps, please cite the following paper to make it known by more people.
@article{jiang2023opencorr,
title={OpenCorr: An open source library for research and development of digital image correlation},
author={Jiang, Zhenyu},
journal={Optics and Lasers in Engineering},
volume={165},
pages={107566},
year={2023},
publisher={Elsevier}
}
We are jubilant at that OpenCorr helps other colleagues in their study as a software development kit or testing benchmark. We would appreciate it if anyone could let us know the work not yet included in this list.
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