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  Opt: A Domain Specific Language for Non-linear Least Squares Optimization in Graphics and Imaging

DeVito, Z., Mara, M., Zollhöfer, M., Bernstein, G., Ragan-Kelley, J., Theobalt, C., et al. (2016). Opt: A Domain Specific Language for Non-linear Least Squares Optimization in Graphics and Imaging. Retrieved from http://arxiv.org/abs/1604.06525.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002B-9AA6-0 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-002C-1952-7
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arXiv:1604.06525.pdf (Preprint), 4MB
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 Creators:
DeVito, Zachary1, Author
Mara, Michael1, Author
Zollhöfer, Michael2, Author              
Bernstein, Gilbert1, Author
Ragan-Kelley, Jonathan1, Author
Theobalt, Christian2, Author              
Hanrahan, Pat1, Author
Fisher, Matthew1, Author
Nießner, Matthias1, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Computer Graphics, MPI for Informatics, Max Planck Society, ou_40047              

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Free keywords: Computer Science, Graphics, cs.GR,Computer Science, Computer Vision and Pattern Recognition, cs.CV,Computer Science, Programming Languages, cs.PL
 Abstract: Many graphics and vision problems are naturally expressed as optimizations with either linear or non-linear least squares objective functions over visual data, such as images and meshes. The mathematical descriptions of these functions are extremely concise, but their implementation in real code is tedious, especially when optimized for real-time performance in interactive applications. We propose a new language, Opt (available under http://optlang.org), in which a user simply writes energy functions over image- or graph-structured unknowns, and a compiler automatically generates state-of-the-art GPU optimization kernels. The end result is a system in which real-world energy functions in graphics and vision applications are expressible in tens of lines of code. They compile directly into highly-optimized GPU solver implementations with performance competitive with the best published hand-tuned, application-specific GPU solvers, and 1-2 orders of magnitude beyond a general-purpose auto-generated solver.

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Language(s): eng - English
 Dates: 2016-04-212016
 Publication Status: Published online
 Pages: 14 p.
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: arXiv: 1604.06525
URI: http://arxiv.org/abs/1604.06525
BibTex Citekey: DeVito1604.06525
 Degree: -

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