Predictive Simulation and Rendering of Material Appearance Changes Induced by Invisible Light

不可见光引起的材料外观变化的预测模拟和渲染

• 批准号: RGPIN-2015-04440
• 负责人: Baranoski, Gladimir
• 金额: $ 1.75万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=612967
• 关键词: Predictive; Simulation; Rendering; Material; Appearance

Computer graphics remains one of the most pervasive areas of computer science, with applications spanning from the production of movies for educational and entertainment purposes to the visualization of scientific and medical data. The pursuit of realism has been a driving force for innovative research developments in this area. In order to generate images with a high degree of realism, it is necessary to accurately simulate light and matter interactions that affect material appearance attributes such as colour, glossiness and translucency. Realistic rendering applications are largely centered at the visible domain, with the resulting images usually depicting materials in their typical original state. However, real materials are subject to prominent changes in their visible appearance attributes, notably due to exposure to invisible light. For example, natural processes affecting the visible appearance of human skin, such as tanning and freckling, can be induced by ultraviolet light, which can also trigger the emission of visible light (fluorescence and phosphorescence phenomena) by man-made and naturally occurring materials such as minerals and chlorophyll. The main goal of this research program is to advance the state of the art in the modeling of material appearance through the predictive simulation of natural processes triggered by exposure to ultraviolet and infrared light. Although it will be grounded in the computer graphics field, the proposed work will have a substantial interdisciplinary component associated with the evaluation and dissemination of its outcomes. In order to be predictive and reliable, simulations of light and matter interactions need to provide physically correct results. Accordingly, we will evaluate our simulation results through comparisons with actual measured data. We will also make our simulation algorithms and supporting biophysical data available online to enable the reproduction and expansion of our findings. Due to the scientific importance of material appearance changes caused by exposure to invisible light, this research program will lead to relevant contributions not only to computer graphics, but also to a wide range of disciplines, from applied optics to remote sensing. By having predictability and reliability as its main guidelines, it will provide reproducible outcomes that one can build on efficiently in order to tackle open questions in these disciplines and practical problems affecting the quality of life worldwide. Examples of such applications include, but are not limited to, the design of new technologies for the automatic screening of severe medical conditions, the development of effective procedures for the early detection of food quality problems, and the formulation of accurate methods for the remote assessment of soil contamination and crop yield. Hence, it will benefit both researchers and the public at large.

计算机图形学仍然是计算机科学中最普遍的领域之一，其应用范围从用于教育和娱乐目的的电影制作到科学和医疗数据的可视化。对现实主义的追求一直是这一领域创新研究发展的驱动力。为了生成具有高度真实感的图像，有必要准确地模拟影响材料外观属性（如颜色，光泽度和透明度）的光和物质相互作用。 真实感渲染应用主要集中在可见光区域，生成的图像通常以其典型的原始状态描绘材料。然而，真实的材料在它们的可见外观属性上会发生显著的变化，特别是由于暴露于不可见光。例如，影响人类皮肤可见外观的自然过程，如晒黑和雀斑，可以由紫外线引起，紫外线也可以触发人造和天然存在的材料如矿物质和叶绿素发射可见光（荧光和磷光现象）。 该研究计划的主要目标是通过预测模拟暴露于紫外线和红外光引发的自然过程来推进材料外观建模的最新技术水平。虽然这项工作将以计算机制图领域为基础，但拟议的工作将有一个与评价和传播其成果有关的实质性跨学科部分。为了具有预测性和可靠性，光和物质相互作用的模拟需要提供物理上正确的结果。因此，我们将通过与实际测量数据的比较来评估我们的模拟结果。我们还将在线提供我们的模拟算法和支持生物物理数据，以复制和扩展我们的发现。 由于暴露于不可见光引起的材料外观变化的科学重要性，该研究计划将导致相关的贡献不仅对计算机图形学，而且对广泛的学科，从应用光学到遥感。通过将可预测性和可靠性作为其主要准则，它将提供可复制的成果，人们可以有效地利用这些成果，以解决这些学科中的未决问题和影响世界各地生活质量的实际问题。这类应用的例子包括，但不限于，设计自动筛查严重疾病的新技术，开发早期发现食品质量问题的有效程序，以及制定远程评估土壤污染和作物产量的准确方法。因此，它将有利于研究人员和广大公众。