Matches in SemOpenAlex for { <https://semopenalex.org/work/W2951396044> ?p ?o ?g. }
- W2951396044 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." @default.
- W2951396044 created "2019-06-27" @default.
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- W2951396044 date "2018-12-17" @default.
- W2951396044 modified "2023-09-23" @default.
- W2951396044 title "Taking a Deeper Look at the Inverse Compositional Algorithm" @default.
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- W2951396044 doi "https://doi.org/10.48550/arxiv.1812.06861" @default.
- W2951396044 hasPublicationYear "2018" @default.
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