Taking a Deeper Look at the Inverse Compositional Algorithm

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. Mo...

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Bibliographic Details
Published in:	Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) pp. 4576 - 4585
Main Authors:	Lv, Zhaoyang, Dellaert, Frank, Rehg, James M., Geiger, Andreas
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.06.2019
Subjects:	3D from Multiview and Sensors Computer vision Data models Integrated circuits Lighting Motion and Tracking Motion estimation Neural networks Optimization Methods Three-dimensional displays
ISSN:	1063-6919
Online Access:	Get full text
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Summary:	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.
ISSN:	1063-6919
DOI:	10.1109/CVPR.2019.00471