A 3D modeling from uncalibrated images algorithm implementation. The algorithm takes a series of features (i.e. 2D coordinates), tracked through a sequence of images (i.e. taken with a common handheld camera), and returns the 3D coordinates of these features in the metric space. To algorithm performs the following steps:

• Selecting two views(images), e.g. i-th and j-th view, the fundamental matrix and the epipoles are computed from the corresponding 2D features of the i-th and j-th view. For best results, the fartherst views in the sequence are selected.
• Estimating the projection matrices for the i-th and j-th view. To do so, the i-th view is assumed to be aligned with the world frame, and the projection matrix for the j-th view can be deduced using the fundamental matrix, the epipole and the reference frame of the reconstruction.
• Triangulation of the 2D features of the i-th and j-th views to get an initial estimate of the 3D structure.
• Estimating the projection matrices for all the remaining views, using the 3D points we got from triangulation.
• Bundle adjustment, i.e. an overall optimization to refine the 3D points and the projection matrices by minimizing the reprojection error of the 3D points back to each view.
• Self-calibration to estimate the camera intrisic parameters for each view, and transform the 3D structure from the projective to metric space.

#Prerequisites# The goal was to make the code as light as possible, so only the following packages are required

• numpy
• pandas
• scipy
• stl