G-LSMEI: GPU-Accelerated Least-Square Matching Refiner for Epipolar Image Pairs

A project of the sub-pixel Least-Square Matching Refining Algorithm for Epipolar-Rectified Stereo Image Pairs in window-size areas. The classic algorithm is proposed by Ackermann in DIGITAL IMAGE CORRELATION: PERFORMANCE AND POTENTIAL APPLICATION IN PHOTOGRAMMETRY (1984), where the correspondence search reduces to a one-dimensional problem along epipolar lines, enabling both fast convergence and high accuracy.

We provide the source code in C++ and a Python API library built upon it. A PyQt Demo Application and the CUDA-based GPU-accelerated version is also provided.
Up to now we have successfully tested the C++ and Python on Windows 10 and Windows 11.

Project Page
Contributors: Haojun Tang, Jiahao Zhou
Acknowledgements: Thanks to the guidance of Yunsheng Zhang from Central South University.

About the Project

Environment

Application Structure

CUDA Executions

Some Results

Matching Results

Left Image	Right Image

Time Costs

Improved by 500+ times in our GPU implementation!

Core Algorithm tested in C++ (only CPU-based)

Prerequisites
MSVC (Visual Studio 2022, with C++ CMake tools), CMake ≥ 3.20, and the source code:

git clone https://github.com/DonaldTrump-coder/G-LSMEI --recursive

Build for the Code
In the project directory, run the following commands:

mkdir build
cd build
cmake .. -G "Visual Studio 17 2022"
cmake --build . --config Release
.\Release\leastsquares_matching.exe

The output is from the main function in core\src\test.cpp
Use the C++ source code for testing the algorithm.

Image Matching Application Deployment (CPU and GPU support)

Prerequisites
You also need MSVC and CMake, conda (or any Python environment), as well as nvcc for CUDA (Already successfully tested on CUDA 12.4). Install them and start to build!

1. Install the Requirement Packages for Python
In the project directory, run

conda create -n matching python=3.11
conda activate matching
pip install -r requirements.txt

2. Build the C++ and CUDA Source for Python Application

cd python
python setup.py bdist_wheel
python -m pip install (Get-ChildItem dist\glsmei-*.whl).FullName --force-reinstall
cd ..

Then the installation of glsmei is done.

3. Use the Application in Python
run

python main.py

API Reference

Installation from pip (If your Python and CUDA environment is supported): pip install glsmei
Verify installation: python -c "import lsmatching; print(lsmatching.__version__)"

Matching in CPU

Matching(left_image_path: str, right_image_path: str)

Matching in GPU

batch_adjust_gpu(window_size=15, d_corr=0.04, max_iter=20, matching_wsize=3, corr_threshold=0.7, savepath=None)

GPU batch pipeline: Feature Extraction → Correlation Matching → Least-Squares Refinement

License

This project is licensed under the Apache License 2.0. See LICENSE details.

Citation

If you use our work or our data in your research, please cite:

@misc{Tang2026GLSMEI,
  title        = {G-LSMEI: GPU-Accelerated Least-Square Matching Refiner for Epipolar Image Pairs},
  author       = {Haojun Tang and Jiahao Zhou},
  year         = {2026},
  howpublished = {\url{https://github.com/DonaldTrump-coder/G-LSMEI}},
  note         = {Version 1.0.2. Apache License 2.0}
}