This is the python port of Dr. Lindstorm's niter2 image-space triangulation method from his 2010 paper. This was done as part of the Final Project for ME 7953 Applied Computational Methods taught by Dr. Ingmar Schoegl at Louisiana State University.

niter2 primarily optimizes matched keypoints by moving them on the epipolar line in such a way that intermediate steps obeys epipolar constraint. Lindstorm later demonstrated that with only two steps, reprojection error may reach as low as machine precision. Thus, niter2 is a two interation process. Please refer to the original paper (given in the repository) for further details.

Niter2 is the class that implements the niter2 algorithm. A detail description of how it was implemented here may be found in the final.pdf report or in final.ipynb Secion 3. Hence, a method to solve the $x_i = P_i X$ is required. In niter2, linear-triangulation based on this lecture is given. Here are the key function calls to use this method

It is highly recommended that either anaconda or miniforge3 with the mamba package manager instead of conda. If you are using ruff, my configurations for it are given in the pyproject.toml file.

• OpenCV >= 4.9.0
• Numpy
• Matplotlib
• Numba
• Natsort
• PyYAML
• Ruff [optional for linting]

• To optimize matched keypoints: Niter2.triangulate_niter2(args**)->Tuple[np.ndarray, np.ndarray]
• To use the provided linear-triangulation: Niter2.linear_triangulate()->np.ndarray
• Check final.ipynb and test.ipynb to see how Niter2 can be used in computer vision project.

• Convert Niter2 into a standalone module, make numba accelerated functions staticmethods and pin the project.
• Release version 1.0 of Niter2
• [] Use CuPy to convert niter2_triangulate() into a GPU accelerated method

• [] Use the Dinosaur dataset to test full 3d point cloud generation https://vision.middlebury.edu/mview/data/
• [] Find out how to do Bundle adjustment to reduce error.
• [] Go thru this project to add chilrety test https://github.com/sakshikakde/SFM
• [] https://unisvalbard.github.io/Geo-SfM/content/lessons/l1/sfm_photogrammetry.html