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2018 | OriginalPaper | Chapter

Learning to Solve Nonlinear Least Squares for Monocular Stereo

Authors : Ronald Clark, Michael Bloesch, Jan Czarnowski, Stefan Leutenegger, Andrew J. Davison

Published in: Computer Vision – ECCV 2018

Publisher: Springer International Publishing

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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 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.

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Title: Learning to Solve Nonlinear Least Squares for Monocular Stereo
Authors: Ronald Clark
Michael Bloesch
Jan Czarnowski
Stefan Leutenegger
Andrew J. Davison
Publisher: Springer International Publishing
Book: Computer Vision – ECCV 2018
Print ISBN: 978-3-030-01236-6

Electronic ISBN: 978-3-030-01237-3

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-01237-3_18

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