M
Michael Mara
Researcher at Facebook
Publications - 29
Citations - 535
Michael Mara is an academic researcher from Facebook. The author has contributed to research in topics: Rendering (computer graphics) & Global illumination. The author has an hindex of 13, co-authored 29 publications receiving 455 citations. Previous affiliations of Michael Mara include Nvidia & Williams College.
Papers
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Efficient GPU Screen-Space Ray Tracing
Morgan McGuire,Michael Mara +1 more
TL;DR: This paper provides for the first time full implementation details of a method that has been proven in production of recent major game titles by adapting the perspective-correct DDA line rasterization algorithm to support multiple depth layers for robustness.
Journal ArticleDOI
Opt: A Domain Specific Language for Non-Linear Least Squares Optimization in Graphics and Imaging
Zachary DeVito,Michael Mara,Michael Zollhöfer,Gilbert Louis Bernstein,Jonathan Ragan-Kelley,Christian Theobalt,Pat Hanrahan,Matthew Fisher,Matthias Niessner +8 more
TL;DR: Opt as discussed by the authors is a language for writing objective functions over image- or graph-structured unknowns concisely and at a high level, which automatically transforms these specifications into state-of-the-art GPU solvers based on Gauss-Newton or Levenberg-Marquardt methods.
Proceedings ArticleDOI
An efficient denoising algorithm for global illumination
TL;DR: A hybrid ray-tracing/rasterization strategy for realtime rendering enabled by a fast new denoising method that enables efficient (biased) reconstruction by denoised light without blurring materials is proposed.
Proceedings ArticleDOI
Real-time global illumination using precomputed light field probes
TL;DR: A new data structure and algorithms that employ it to compute real-time global illumination from static environments, and applies ideas from screen-space and voxel cone tracing techniques to efficiently sample radiance on world space rays, with correct visibility information, directly within pixel and compute shaders.
A Survey of Efficient Representations for Independent Unit Vectors
TL;DR: Time- and space-efficient representations for the important case of non-register, in-core, statistically independent unit vectors, with emphasis on GPU encoding and decoding are surveyed.