Taking a Deeper Look at the Inverse Compositional Algorithm
This work addresses image alignment for computer vision applications, but it is incremental as it builds on a well-established technique.
The paper tackles dense image alignment by relaxing assumptions of the classic inverse compositional algorithm through data-driven priors, resulting in improved performance on 3D rigid motion estimation tasks that outperforms both the classic algorithm and data-driven regression approaches.
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.