Paper Info
Title
Opt: A Domain Specific Language for Non-linear Least Squares Optimization in Graphics and Imaging.
Abstract
Many graphics and vision problems can be expressed as non-linear least squares optimizations of 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 on modern GPUs in interactive applications. In this work, we propose a new language, Opt,1 for writing these objective functions over image- or graph-structured unknowns concisely and at a high level. Our compiler automatically transforms these specifications into state-of-the-art GPU solvers based on Gauss-Newton or Levenberg-Marquardt methods. Opt can generate different variations of the solver, so users can easily explore tradeoffs in numerical precision, matrix-free methods, and solver approaches. In our results, we implement a variety of real-world graphics and vision applications. Their energy functions are expressible in tens of lines of code and produce highly optimized GPU solver implementations. These solvers are competitive in performance with the best published hand-tuned, application-specific GPU solvers, and orders of magnitude beyond a general-purpose auto-generated solver.
Year
DOI
Venue
2017
10.1145/3132188
international conference on computer graphics and interactive techniques
Keywords
DocType
Volume
Domain-specific languages, Gauss-Newton, Levenberg-Marquardt, non-linear least squares, real-time optimization
Journal
abs/1604.06525
Issue
ISSN
Citations 
5
0730-0301
6
PageRank 
References 
Authors
0.41
26
9
Name
Order
Citations
PageRank
Zachary DeVito120710.71
Michael Mara2364.52
Michael Zollhöfer385242.25
Gilbert Louis Bernstein4764.34
Jonathan Ragan-Kelley565529.77
Christian Theobalt63211159.16
Pat Hanrahan7110811148.97
Matthew Fisher875736.98
Matthias Nießner9137367.28