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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Veröffentlicht in:Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) S. 4576 - 4585
Hauptverfasser: Lv, Zhaoyang, Dellaert, Frank, Rehg, James M., Geiger, Andreas
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.06.2019
Schlagworte:
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
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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.
AbstractList 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.
Author Lv, Zhaoyang
Rehg, James M.
Geiger, Andreas
Dellaert, Frank
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  fullname: Dellaert, Frank
  organization: Georgia Tech
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  givenname: James M.
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  fullname: Rehg, James M.
  organization: Georgia Institute of Technology
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  givenname: Andreas
  surname: Geiger
  fullname: Geiger, Andreas
  organization: MPI-IS and Univ. of Tuebingen
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Snippet 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...
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SubjectTerms 3D from Multiview and Sensors
Computer vision
Data models
Integrated circuits
Lighting
Motion and Tracking
Motion estimation
Neural networks
Optimization Methods
Three-dimensional displays
Title Taking a Deeper Look at the Inverse Compositional Algorithm
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