  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 LS-Net, a neural
nonlinear least squares optimization algorithm which learns to effectively
optimize these cost functions even in the presence of adversities. Unlike
traditional approaches, 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.
