ITR/AP: Beyond Polygons and Pixels: New Paradigms for Real-Time, Physically-Based Rendering

ITR：除了多边形和像素：实时、基于物理的渲染新范式本研究提案的最终目标是提供实时、物理精确的合成图像，以完全交互的速率提供突破性的真实感。我们将通过将并行图形渲染的新方法与人类视觉的高级特征级心理物理模型和新的图像表示相结合来实现这一目标。目前，图像合成的两个极端是基于物理的渲染，其中对光反射的精确模拟给出了场景外观的忠实预测，以及实时渲染，其中允许对精确模拟的粗略近似以提供交互速率的动态图像。在当今的工作站上，包含间接照明、表面之间的相互反射和渗色的高质量、物理上精确的图像可能需要数小时甚至数天的时间来计算，并且渲染速度的增量改进不足以弥合差距。我们估计，复杂场景的全局照明的实时模拟可能需要10 - 7倍以上的处理能力，比我们今天的多处理器工作站。这只能通过对我们如何生成、编码和显示合成图像采取完全不同的方法来实现，这种方法通过光反射分量分离计算，并且基于人类视觉的高级心理物理模型和新的图像表示。低成本、高效流水线的图形加速器板已经变得非常流行，但是这些架构仅处理局部照明分量，因此不能提供全局照明效果或保证物理精度。为了解决这一缺点，我们开发了新的算法，利用这些加速器板的速度直接照明，同时在现成的英特尔微处理器集群上并行执行全局照明计算。对于复杂环境，我们已经将计算时间从数小时减少到数分钟，但对于实时图像合成，我们需要另外四个数量级的加速。为了实现这一目标，我们必须开发更先进的人类视觉模型。当前的感知驱动渲染方法基于人类视觉阈值模型，该模型预测我们区分亮度对比度、空间模式、运动和颜色，但没有提供优化渲染操作的顺序或精度的指导。对于我们新的感知预言机，我们正在开发更高级别的视觉模型，以监控场景特征（如阴影和反射）对感知图像质量的重要性。这些新模型将驱动并行计算资源的分配，并选择合适的算法来提供“最陡上升”的解决方案。新的图像表示数据结构将包括照明和对比度梯度以及逐像素强度，以确保在所有观看条件下物理上准确和感知上无法区分的解决方案的最佳显示。这些功能将把图形仿真的科学、教育和商业应用扩展到视觉关键任务中，在这些任务中，预测的可靠性和速度至关重要。

ITR: Beyond Polygons and Pixels: New Paradigms for Real-Time, Physically-Based RenderingThe ultimate goal of this research proposal is to provide real-time, physically accurate synthetic images, delivering breakthrough realism at fully interactive rates. We will achieve this by combining new approaches for parallel graphics rendering with advanced, feature-level psychophysical models of human vision and new image representations. Currently, the two extremes of image synthesis are physically-based rendering, where accurate simulation of light reflection gives faithful predictions of the appearance of scenes, and real-time rendering, where crude approximations to accurate simulation are tolerated to provide dynamic imagery at interactive rates. High-quality, physically accurate images incorporating indirect lighting, inter-reflections between surfaces, and color bleeding can take hours or even days to compute on today's workstations, and incremental improvements to the speed of rendering will not be enough to bridge the gap. We estimate that real-time simulations of global illumination for complex scenes might require 10 7 times more processing power than we have on multi-processor workstations today. This can only be achieved by taking a radically different approach to how we generate, encode, and display synthetic images, an approach that separates computation by light reflection components and is based on advanced psychophysical models of human vision and new image representations.Low-cost, efficiently pipelined graphics accelerator boards have become extremely popular, but these architectures only process local illumination components, and thus cannot provide global illumination effects or guarantee physical accuracy. To address this shortcoming, we have developed new algorithms that exploit the speed of these accelerator boards for direct lighting while performing global illumination computations in parallel on clusters of off-the-shelf Intel microprocessors. We have reduced computation times from hours to minutes for complex environments, but for real-time image synthesis we need another four orders of magnitude speed-up.To reach this goal, we must develop more advanced models of human visual perception.Current perceptually-driven rendering methods are based on threshold models of human vision that predict the limits of our abilities to discriminate luminance contrasts, spatial patterns, motions, and colors, but provide no guidance for optimizing the order or precision of rendering operations. For our new perception oracles, we are developing higher-level visual models to monitor the importance of scene features such as shadows and reflections to perceived image quality. These new models will then drive the allocation of parallel computing resources as well as select appropriate algorithms to provide the "steepest ascent" solutions. New data structures for pictorial representation will incorporate illumination and contrast gradients as well as pixel-by-pixel intensities to ensure optimal display of physically accurate and perceptually indistinguishable solutions under all viewing conditions. These capabilities will extend the scientific, educational, and commercial application of graphical simulations into visually critical tasks where predictive reliability and speed are paramount.