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

ITR/AP：超越多边形和像素：实时、基于物理的渲染的新范式

• 批准号: 0205438
• 负责人: Donald Greenberg
• 金额: --
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-07-01 至 2009-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0205438&HistoricalAwards=false
• 关键词: ITR; AP; Beyond; Polygons; Pixels

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.

ITR：超越多边形和像素：实时，基于物理的渲染的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可以忠实地预测场景的外观，以及实时渲染，其中可以容忍对精确模拟的粗略近似，以交互速率提供动态图像。在今天的工作站上，包含间接照明、表面之间的相互反射和色彩溢出的高质量、物理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将不足以弥补差距。我们估计，对于复杂场景的全局照明的实时模拟，可能需要比我们今天的多处理器工作站多10.7倍的处理能力。这只能通过采取一种完全不同的方法来实现，即我们如何生成、编码和显示合成图像，这种方法通过光反射组件分离计算，并基于人类视觉和新图像表示的先进心理物理模型。低成本、高效流水线的图形加速板已经变得非常流行，但这些架构只处理局部照明组件，因此无法提供全局照明效果或保证物理精度。为了解决这个缺点，我们开发了新的算法，利用这些加速器板的速度进行直接照明，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。对于复杂的环境，我们已经将计算时间从几小时减少到几分钟，但对于实时图像合成，我们还需要另外四个数量级的加速。为了达到这个目标，我们必须开发更先进的人类视觉感知模型。当前的感知驱动渲染方法是基于人类视觉的阈值模型，该模型预测了我们区分亮度对比、空间模式、运动和颜色的能力的极限，但没有提供优化渲染操作的顺序或精度的指导。对于我们的新感知预言机，我们正在开发更高层次的视觉模型，以监控场景特征（如阴影和反射）对感知图像质量的重要性。然后，这些新模型将驱动并行计算资源的分配，并选择适当的算法来提供“最陡上升”的解决方案。用于图像表示的新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精确和感知上不可区分的解决方案的最佳显示。这些功能将把图形模拟的科学、教育和商业应用扩展到预测可靠性和速度至关重要的视觉关键任务中。