Taking a Deeper Look at the Inverse Compositional Algorithm

Zhaoyang Lv, Frank Dellaert, James M. Rehg, Andreas Geiger; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 4581-4590

Abstract


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.

Related Material


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[bibtex]
@InProceedings{Lv_2019_CVPR,
author = {Lv, Zhaoyang and Dellaert, Frank and Rehg, James M. and Geiger, Andreas},
title = {Taking a Deeper Look at the Inverse Compositional Algorithm},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}