Abstract. Sum-of-squares objective functions are very popular in computer vision algorithms. However, these objective functions are not always easy to optimize. The underlying assumptions made by solvers are often not satisfied and
many problems are inherently ill-posed. In this paper, we propose a neural nonlinear least squares optimization algorithm which learns to effectively optimize
these cost functions. The proposed solver requires no hand-crafted regularizers
or priors as these are implicitly learned from the data. We apply our method to
the problem of motion stereo ie. jointly estimating the motion and scene geometry from pairs of images of a monocular sequence. We show that our learned
optimizer is able to efficiently and effectively solve this challenging optimization
problem.