CAREER: Scalable Rendering for Visual Realism in Scale-Complex Scenes

职业：在规模复杂的场景中实现视觉真实感的可扩展渲染

• 批准号: 0644175
• 负责人: Kavita Bala
• 金额: $ 45万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-02-01 至 2013-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0644175&HistoricalAwards=false
• 关键词: CAREER; Scalable; Rendering; Visual; Realism

CAREER: Scalable Rendering for Visual Realism in Scale-Complex ScenesPI: Kavita BalaA fundamental challenge in computer graphics is to create interactive virtual environments that accurately depict the complex natural scenes of the real world. These virtual environments are vital for a wide variety of applications, including e-commerce, education, industrial design and architectural planning, games and movies, safety analysis and virtual training, and cultural heritage. Realistically simulating the visual appearance of the real world is extremely challenging because scenes of interest have complex geometry, material, and lighting interacting across a wide range of physical scales, ranging from millimeter-sized surface bumps to large-scale structure. We call such scenes scale-complex. Current rendering methods are blind to scale, making it infeasible to realistically simulate the complex paths along which light reflects and scatters in such scale-complex scenes. This project develops a novel framework for realistically rendering images of scale-complex scenes. Importantly, the framework supports rich illumination phenomena and rendering effects such as indirect illumination, participating media, subsurface scattering, motion blur, and depth-of-field.For the proposed framework to be scalable, it must perform well even with growing complexity of the scene and of simulated illumination phenomena. This project explores the following new approaches: (a) a unified treatment of all illumination phenomena and rendering effects, (b) novel multiresolution representations coupled with perceptual metrics based on early vision and higher level vision to eliminate computation where it is not visually important, (c) new methods for accurately computing illumination detail as needed, with illumination-driven simplification of geometry and material, and (d) new hybrid CPU/GPU algorithms for interactive performance.

职业：在规模复杂场景中的视觉现实主义的可扩展渲染：计算机图形中的Kavita Balaa基本挑战是创建交互式虚拟环境，以准确描述现实世界中复杂的自然场景。 这些虚拟环境对于各种应用至关重要，包括电子商务，教育，工业设计和建筑计划，游戏和电影，安全分析和虚拟培训以及文化遗产。 现实地模拟现实世界的视觉外观非常具有挑战性，因为感兴趣的场景在各种物理尺度上具有复杂的几何形状，材料和照明相互作用，从毫米大小的表面颠簸到大型结构。我们称此类场景比例复合。 当前的渲染方法对尺度视而不见，这使得在这种比例复杂的场景中现实模拟光反射和散射的复杂路径是不可行的。 该项目为实际渲染比例复合场景的图像开发了一个新颖的框架。重要的是，该框架支持丰富的照明现象和渲染效果，例如间接照明，参与介质，地下散射，运动模糊和景点。为了使所提出的框架具有可扩展性，即使在现场和模拟照明现象的场景和现场的复杂性变得越来越复杂。 This project explores the following new approaches: (a) a unified treatment of all illumination phenomena and rendering effects, (b) novel multiresolution representations coupled with perceptual metrics based on early vision and higher level vision to eliminate computation where it is not visually important, (c) new methods for accurately computing illumination detail as needed, with illumination-driven simplification of geometry and material, and (d) new hybrid互动性能的CPU/GPU算法。