557 research outputs found
The lightspeed automatic interactive lighting preview system
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 57-59).We present an automated approach for high-quality preview of feature-film rendering during lighting design. Similar to previous work, we use a deep-framebuffer shaded on the GPU to achieve interactive performance. Our first contribution is to generate the deep-framebuffer and corresponding shaders automatically through data-flow analysis and compilation of the original scene. Cache compression reduces automatically-generated deep-framebuffers to reasonable size for complex production scenes and shaders. We also propose a new structure, the indirect framebuffer, that decouples shading samples from final pixels and allows a deep-framebuffer to handle antialiasing, motion blur and transparency efficiently. Progressive refinement enables fast feedback at coarser resolution. We demonstrate our approach in real-world production.by Jonathan Millard Ragan-Kelley.S.M
OpenFab: A programmable pipeline for multimaterial fabrication
Figure 1: Three rhinos, defined and printed using OpenFab. For each print, the same geometry was paired with a different fablet—a shaderlike program which procedurally defines surface detail and material composition throughout the object volume. This produces three unique prints by using displacements, texture mapping, and continuous volumetric material variation as a function of distance from the surface. 3D printing hardware is rapidly scaling up to output continuous mixtures of multiple materials at increasing resolution over ever larger print volumes. This poses an enormous computational challenge: large high-resolution prints comprise trillions of voxels and petabytes of data and simply modeling and describing the input with spatially varying material mixtures at this scale is challenging. Existing 3D printing software is insufficient; in particular, most software is designed to support only a few million primitives, with discrete material choices per object. We present OpenFab, a programmable pipeline for synthesis of multi-material 3D printed objects that is inspired by RenderMan and modern GPU pipelines. The pipeline supports procedural evaluation of geometric detail and material composition, using shader-like fablets, allowing models to be specified easily and efficiently. We describe a streaming architecture for OpenFab; only a small fraction of the final volume is stored in memory and output is fed to the printer with little startup delay. We demonstrate it on a variety of multi-material objects
Decoupled Sampling for Graphics Pipelines
We propose a generalized approach to decoupling shading from visibility sampling in graphics pipelines, which we call decoupled sampling. Decoupled sampling enables stochastic supersampling of motion and defocus blur at reduced shading cost, as well as controllable or adaptive shading rates which trade off shading quality for performance. It can be thought of as a generalization of multisample antialiasing (MSAA) to support complex and dynamic mappings from visibility to shading samples, as introduced by motion and defocus blur and adaptive shading. It works by defining a many-to-one hash from visibility to shading samples, and using a buffer to memoize shading samples and exploit reuse across visibility samples. Decoupled sampling is inspired by the Reyes rendering architecture, but like traditional graphics pipelines, it shades fragments rather than micropolygon vertices, decoupling shading from the geometry sampling rate. Also unlike Reyes, decoupled sampling only shades fragments after precise computation of visibility, reducing overshading.
We present extensions of two modern graphics pipelines to support decoupled sampling: a GPU-style sort-last fragment architecture, and a Larrabee-style sort-middle pipeline. We study the architectural implications of decoupled sampling and blur, and derive end-to-end performance estimates on real applications through an instrumented functional simulator. We demonstrate high-quality motion and defocus blur, as well as variable and adaptive shading rates
Opt: A Domain Specific Language for Non-linear Least Squares Optimization in Graphics and Imaging
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 (available under
http://optlang.org), 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 solver have performance
competitive with the best published hand-tuned, application-specific GPU
solvers, and orders of magnitude beyond a general-purpose auto-generated
solver
Decoupled Sampling for Real-Time Graphics Pipelines
We propose decoupled sampling, an approach that decouples shading from visibility sampling in order to enable motion blur and depth-of-field at reduced cost. More generally, it enables extensions of modern real-time graphics pipelines that provide controllable shading rates to trade off quality for performance. It can be thought of as a generalization of GPU-style multisample antialiasing (MSAA) to support unpredictable shading rates, with arbitrary mappings from visibility to shading samples as introduced by motion blur, depth-of-field, and adaptive shading. It is inspired by the Reyes architecture in offline rendering, but targets real-time pipelines by driving shading from visibility samples as in GPUs, and removes the need for micropolygon dicing or rasterization. Decoupled Sampling works by defining a many-to-one hash from visibility to shading samples, and using a buffer to memoize shading samples and exploit reuse across visibility samples. We present extensions of two modern GPU pipelines to support decoupled sampling: a GPU-style sort-last fragment architecture, and a Larrabee-style sort-middle pipeline. We study the architectural implications and derive end-to-end performance estimates on real applications through an instrumented functional simulator. We demonstrate high-quality motion blur and depth-of-field, as well as variable and adaptive shading rates
Comparison of Primary Care Physician Reimbursement Rates in the United States
With a growing shortage of physicians, particularly primary care physicians, the issue of adequate pay in Hawai‘i is increasingly important. Anecdotal reports of low pay in Hawai‘i have rarely been substantiated. Data from FAIR Health, a company that tracks private insurance reimbursement rates, is compared across the United States (US) for the CPT code 99213. In addition, FAIR Health and Medicare rates are compared for cities with both similar and disparate cost of living to Hawai‘i. Hawai‘i is in the second lowest quintile for payment in the US for private insurances, and providers are reimbursed significantly lower than in cities with similar cost of living by both Medicare and private insurances. Methods for increasing payment to physicians in Hawai‘i are essential to recruiting the necessary workforce. Revising payment methodologies that increase pay for services in areas of unmet need, revising Medicare Geographic Price Cost Indices to better balance pay in areas of need, and making use of the 10% Medicare Bonus Program for physicians working in Health Professions Shortage Areas are first steps to creating a sustainable plan for physician payment in the future
Inferring the Future by Imagining the Past
A single panel of a comic book can say a lot: it shows not only where
characters currently are, but also where they came from, what their motivations
are, and what might happen next. More generally, humans can often infer a
complex sequence of past and future events from a *single snapshot image* of an
intelligent agent.
Building on recent work in cognitive science, we offer a Monte Carlo
algorithm for making such inferences. Drawing a connection to Monte Carlo path
tracing in computer graphics, we borrow ideas that help us dramatically improve
upon prior work in sample efficiency. This allows us to scale to a wide variety
of challenging inference problems with only a handful of samples. It also
suggests some degree of cognitive plausibility, and indeed we present human
subject studies showing that our algorithm matches human intuitions in a
variety of domains that previous methods could not scale to
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