In this paper, we provide a modern synthesis of the classic inverse compositional algorithm for dense image alignment. We first discuss the assumptions made by this well-established technique, and subsequently propose to relax these assumptions by incorporating data-driven priors into this model. More specifically, we unroll a robust version of the inverse compositional algorithm and replace multiple components of this algorithm using more expressive models whose parameters we train in an end-to-end fashion from data. Our experiments on several challenging 3D rigid motion estimation tasks demonstrate the advantages of combining optimization with learning-based techniques, outperforming the classic inverse compositional algorithm as well as data-driven image-to-pose regression approaches.
@article{arxiv.1812.06861,
title = {Taking a Deeper Look at the Inverse Compositional Algorithm},
author = {Zhaoyang Lv and Frank Dellaert and James M. Rehg and Andreas Geiger},
journal= {arXiv preprint arXiv:1812.06861},
year = {2019}
}
Comments
Paper accepted at CVPR 2019, oral presentation. Code is available at https://github.com/lvzhaoyang/DeeperInverseCompositionalAlgorithm