Large scale bundle adjustment in scipy

bundle_adjustment_scipy.ipynb

Thanks for sharing!
You should add that the notebook is for Python 3, so others can know beforehand :-)
Best

This is a really easy tutorial on how to python can be optimized to solve large-scale, sparse problems.

One short-coming i seem to have run into with scipy.least_squares and bundle adjustment in particular, is that i don't see a clear way to apply weights to the minimization. Yes, i can constrain the bounds of the parameters, but in some bundle adjustment applications, it's necessary to weight the observation itself (e.g. a very well known pixel), rather than just constrain the parameter. All this would involve is the pre-multiplication of the jacobian and the residual vector by a weight matrix. However, I've tried various ways and have not achieved the desired result.

Any ideas?

@TungTNguyen the code works for Python 2.7.13 if these two changes are made:

Change line 2 in[4] to: urllib.urlopen(URL, FILE_NAME)
Change line 2 in[5] to: with bz2.BZ2File(file_name, "r") as file:

Tested on mid 2012 Macbook pro with OS version 10.13.6.