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

人们非常关心半透明材料的外观，如食物，皮肤，肥皂和大理石，他们能够根据外观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，这对模拟具有挑战性，特别是因为人类对它们的细微之处非常敏感。 由于在自然界中，散射材料是正常的，而不是例外，因此人类视觉系统经过精心设计可以分析它们是有道理的。 然而，人们对这种分析是如何实现的知之甚少，因为体积半透明的感知几乎未被研究。这个合作项目涉及来自三所大学的教师，在计算机图形学、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，解决了理解图形半透明性的根本未解决问题。 PI将开发一个感知驱动的管道，为计算机图形学提供新的散射表示，感知维度和计算算法。 基于多分散模型的散射表示将为半透明介质的波长相关体散射特性提供解析表达式;这将显着扩展可以以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）与受控感知研究相结合，可以找到将物理散射参数与视觉外观相关联的感知旋钮。 将创建采用高光谱成像的新型采集方法，以及使用新的感知表示的编辑和渲染应用程序。 将开发和使用低维模型来表示散射介质，以实现高效和准确的采集和渲染。 将开发一套测试材料和场景，以基于开发的理论和计算应用程序评估渲染图像的保真度。 目前，半透明性的模拟在计算和视觉保真度方面都提出了挑战。 这限制了实际算法预测性地模拟半透明材料的能力，从而从根本上限制了图形在真实的应用中的使用。通过构建计算工具来表征，研究和使用半透明感知的知识，这项研究将从根本上改变半透明材料的图形管道。并将有可能彻底改变工业设计、室内设计、皮肤护理和化妆品以及娱乐。该项目包括一个与研究计划紧密结合的教育计划。 PI已经每周开会两次超过六个月，他们的研究生已经共享数据，代码和设备。 在活动期间，学生们将进行为期一周和一个月的访问彼此的实验室进行合作，通过这种方式，该项目将产生一代“T形”的研究人员，他们既深入各自的领域，又能够在这些协同学科中有效地工作。 PI还计划组织一个研讨会，将视觉科学，计算机图形学和计算机视觉的研究人员聚集在一起，以便进一步加强这些领域之间的重要联系。

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.