Peer Reviewed: The Difficult Challenge of Attaining EPA's New Ozone Standard

Air Quality.     

Peer Reviewed: Science, Uncertainty, and EPA's New Ozone Standards

There are still gray areas in the science about how ozone harms human health and vegetation.     

Adaptive Volumetric Shadow Maps

We introduce adaptive volumetric shadow maps (AVSM), a real‐time shadow algorithm that supports high‐quality shadowing from dynamic volumetric media such as hair and smoke. The key contribution of AVSM is the introduction of a streaming simplification algorithm that generates an accurate volumetric light attenuation function using a small fixed memory footprint. This compression strategy leads to high performance because the visibility data can remain in on‐chip memory during simplification and can be efficiently sampled during rendering. We demonstrate that AVSM compression closely approximates the ground‐truth correct solution and performs competitively to existing real‐time rendering techniques while providing higher quality volumetric shadows.     

An ocularist's approach to human iris synthesis

We have a particularly fortunate situation in iris synthesis: artificial eye makers (ocularists) have developed a procedure for physical iris synthesis that results in eyes with all the important appearance characteristics of real eyes. They have refined this procedure over decades,and the performance of their products in the real world completely validates the approach. Our approach lets users (other than trained ocularists) create a realistic looking human eye, paying particular attention to the iris. We draw from domain knowledge provided by ocularists to provide a toolkit that composes a human iris by layering semitransparent textures. These textures look decidedly painted and unrealistic. The composited result, however, provides a sense of depth to the iris and takes on a level of realism that we believe others have not previously achieved. Prior work on rendering eyes has concentrated predominantly on producing geometry for facial animation or for medical applications. Some work has focused on accurately modeling the cornea. In contrast, the goal of our work is the easy creation of realistic looking irises for both the ocular prosthetics and entertainment industries.     

Establishing policy relevant background (PRB) ozone concentrations in the United States

Policy Relevant Background (PRB) ozone concentrations are defined by the United States (U.S.) Environmental Protection Agency (EPA) as those concentrations that would occur in the U.S. in the absence of anthropogenic emissions in continental North America (i.e., the U.S, Canada, and Mexico). Estimates of PRB ozone have had an important role historically in the EPA's human health and welfare risk analyses used in establishing National Ambient Air Quality Standards (NAAQS). The margin of safety for the protection of public health in the ozone rulemaking process has been established from human health risks calculated based on PRB ozone estimates. Sensitivity analyses conducted by the EPA have illustrated that changing estimates of PRB ozone concentrations have a progressively greater impact on estimates of mortality risk as more stringent standards are considered. As defined by the EPA, PRB ozone is a model construct, but it is informed by measurements at relatively remote monitoring sites (RRMS). This review examines the current understanding of PRB ozone, based on both model predictions and measurements at RRMS, and provides recommendations for improving the definition and determination of PRB ozone.     

A streaming narrow-band algorithm: interactive computation and visualization of level sets

Deformable isosurfaces, implemented with level-set methods, have demonstrated a great potential in visualization and computer graphics for applications such as segmentation, surface processing, and physically-based modeling. Their usefulness has been limited, however, by their high computational cost and reliance on significant parameter tuning. This paper presents a solution to these challenges by describing graphics processor (GPU) based algorithms for solving and visualizing level-set solutions at interactive rates. The proposed solution is based on a new, streaming implementation of the narrow-band algorithm. The new algorithm packs the level-set isosurface data into 2D texture memory via a multidimensional virtual memory system. As the level set moves, this texture-based representation is dynamically updated via a novel GPU-to-CPU message passing scheme. By integrating the level-set solver with a real-time volume renderer, a user can visualize and intuitively steer the level-set surface as it evolves. We demonstrate the capabilities of this technology for interactive volume segmentation and visualization.     

Neural Temporal Adaptive Sampling and Denoising

Despite recent advances in Monte Carlo path tracing at interactive rates, denoised image sequences generated with few samples per-pixel often yield temporally unstable results and loss of high-frequency details. We present a novel adaptive rendering method that increases temporal stability and image fidelity of low sample count path tracing by distributing samples via spatio-temporal joint optimization of sampling and denoising. Adding temporal optimization to the sample predictor enables it to learn spatio-temporal sampling strategies such as placing more samples in disoccluded regions, tracking specular highlights, etc; adding temporal feedback to the denoiser boosts the effective input sample count and increases temporal stability. The temporal approach also allows us to remove the initial uniform sampling step typically present in adaptive sampling algorithms. The sample predictor and denoiser are deep neural networks that we co-train end-to-end over multiple consecutive frames. Our approach is scalable, allowing trade-off between quality and performance, and runs at near real-time rates while achieving significantly better image quality and temporal stability than previous methods.     

A Survey of General-Purpose Computation on Graphics Hardware

The rapid increase in the performance of graphics hardware, coupled with recent improvements in its programmability, have made graphics hardware a compelling platform for computationally demanding tasks in a wide variety of application domains. In this report, we describe, summarize, and analyze the latest research in mapping general‐purpose computation to graphics hardware. We begin with the technical motivations that underlie general‐purpose computation on graphics processors (GPGPU) and describe the hardware and software developments that have led to the recent interest in this field. We then aim the main body of this report at two separate audiences. First, we describe the techniques used in mapping general‐purpose computation to graphics hardware. We believe these techniques will be generally useful for researchers who plan to develop the next generation of GPGPU algorithms and techniques. Second, we survey and categorize the latest developments in general‐purpose application development on graphics hardware.