CGV: Medium: Collaborative Research: Understanding Translucency: Physics, Perception, and Computation

CGV：媒介：协作研究：理解半透明性：物理、感知和计算

• 批准号: 1161564
• 负责人: Todd Zickler
• 金额: $ 38.97万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1161564&HistoricalAwards=false
• 关键词: CGV; Medium; Collaborative; Research; Understanding

People care greatly about the appearance of translucent materials such as food, skin, soap, and marble, and they are able to distinguish subtle differences in these materials based on their appearance. The translucent appearance of these materials is caused by internal volumetric scattering, which is challenging to simulate, especially because humans are so sensitive to their subtleties. Since in the natural world scattering materials are the norm, not the exception, it makes sense that the human visual system is so well engineered to analyze them. However, very little is known about how this analysis is achieved because the perception of volumetric translucency is almost unstudied.This collaborative project, involving faculty from three universities with complementary expertise in computer graphics, human vision, machine learning, and computer vision, addresses the fundamental unsolved problem of understanding translucency for graphics. The PIs will develop a perceptually-motivated pipeline for translucency, contributing new scattering representations, perceptual dimensions, and computational algorithms to computer graphics. The scattering representations, based on a polydispersion model, will provide analytic expressions for wavelength-dependent bulk scattering properties of translucent media; this will significantly expand the range of materials that can be simulated with high visual fidelity. Finding perceptual knobs that relate physical scattering parameters with visual appearance will be achieved by coupling large-scale computation (using cloud computing) with controlled perceptual studies. Novel acquisition approaches that employ hyperspectral imaging will be created, as will editing and rendering applications that use the new perceptual representations of translucency. Low-dimensional models to represent scattering media will be developed and used to enable efficient and accurate acquisition and rendering. A suite of test materials and scenes will be developed to evaluate the fidelity of rendered images based on the developed theory and computational applications.Broader Impacts: Currently, the simulation of translucency presents challenges in terms of both computation and visual fidelity. This restricts the ability of practical algorithms to predictively simulate translucent materials, thus fundamentally limiting the use of graphics in real applications. By building the computational tools to characterize, study, and use knowledge of translucency perception, this research will fundamentally change the graphics pipeline for translucent materials. and will potentially revolutionizing industrial design, interior design, skin care and cosmetics, and entertainment.The project includes an education program that is tightly coupled to the research program. The PIs have already been meeting twice a week for more than six months, and their graduate students already share data, code, and equipment. During the activity, the students will make week-long and month-long visits to each other's laboratories to collaborate, and in this way the project will produce a generation of researchers who are "T-shaped" in the sense of being both deep in their respective fields and able to work effectively across these synergistic disciplines. The PIs also plan to organize a workshop that will brins together researchers in vision science, computer graphics, and computer vision, so that the important ties between these fields are strengthened even further.

人们非常关心食物，皮肤，肥皂和大理石等半透明材料的外观，并且能够根据其外观来区分这些材料的细微差异。 这些材料的半透明外观是由内部体积散射引起的，这是一项挑战的模拟，尤其是因为人类对其微妙之处是如此敏感。 由于在自然世界中，散射材料是常态，而不是例外，因此人类视觉系统的设计非常好，可以分析它们。 但是，对于如何实现这种分析，几乎没有了解这种分析，因为体积半透明的感知几乎没有被研究。该协作项目涉及来自三所大学的教职员工，这些大学在计算机图形，人类视觉，机器学习和计算机视觉方面具有互补的专业知识，解决了理解绘画的基本态度的固定问题。 PI将开发出一种感知动机的管道，以进行半透明，贡献新的散射表示，感知维度和计算算法对计算机图形。 基于多分散模型的散射表示形式将为透明培养基的波长依赖性散装特性提供分析表达式。这将大大扩大可以用高视觉保真度模拟的材料范围。 通过将大规模计算（使用云计算）与受控的感知研究耦合，找到将物理散射参数与视觉外观相关的感知旋钮将实现。 将创建采用高光谱成像的新颖获取方法，以及使用透明度的新知觉表示的编辑和渲染应用程序。 将开发并使用低维模型来代表散射介质，以实现有效，准确的获取和渲染。 将开发一套测试材料和场景，以根据开发的理论和计算应用来评估渲染图像的忠诚度。BRODER的影响：当前，对透明度的模拟在计算和视觉保真度方面呈现出挑战。 这限制了实际算法预测模拟半透明材料的能力，从而从根本上限制了在实际应用中使用图形的能力。通过构建表征，研究和使用半透明感知知识的计算工具，这项研究将从根本上改变半透明材料的图形管道。并有可能革新工业设计，室内设计，护肤和化妆品以及娱乐。该项目包括一个与研究计划紧密结合的教育计划。 PI已经每周两次开会超过六个月，他们的研究生已经共享数据，代码和设备。 在活动期间，学生们将对彼此的实验室进行长达一周和一个月的访问，以便在这种方式上，该项目将在各自的领域深入研究，并能够在这些协同的学科中有效地工作。 PI还计划组织一个研讨会，将视觉科学，计算机图形和计算机视觉研究人员融合在一起，以便进一步加强这些领域之间的重要联系。