Hi, Julia newcomer here
I’m working on learning both Julia and computer vision by implementing common vision algorithms like camera calibration in the Julia language. I’m at a point right now where I’m looking for a nonlinear least-squares framework designed to handle large-scale, sparse problems. Personally, I feel a little overwhelmed by all the options Julia offers (Optim.jl, JSO, NLSolve, JuMP, Minpack, LsqFit, etc…) and I’m wondering if anyone else has done camera calibration/structure-from-motion/bundle adjustment problems in Julia and is willing to share their experiences? It seems to me that the listed libraries above seem to share a lot of common functionality, and its not totally clear to me how one goes about choosing a particular framework?
So far https://github.com/matthieugomez/LeastSquaresOptim.jl seems the most promising option that best fits my use case, but I was wondering if anyone else has used JuMP, Optim, etc… before for my kind of NLS problem?