Illustrative Graphics & Visualization Framework for Geological-Inspired Conceptual Model Development

说明性图形

• 批准号: RGPIN-2018-06423
• 负责人: CostaSousa, Mario
• 金额: $ 2.48万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=648122
• 关键词: Illustrative; Graphics; Visualization; Framework; Geological

***In the early stages of any modeling activity, users collaboratively build a range of conceptual models, which help formulate ideas, suggest possible solutions, and make important decisions that affect subsequent modeling stages. Conceptual models are commonly represented as hand-made sketches and renderings, providing rich visual references for exploring the uncertainties and helping on the depiction of a more interpretive, approximated version of the intended model. Computational tools proposed to aid the user in this process have their foundations in the computer graphics and visualization fields of sketch-based interfaces and modeling (SBIM), non-photorealistic rendering (NPR), and illustrative visualization (IVis). These areas introduced a new paradigm that leverages the traditional illustrator's fundamental and applied drawing and rendering skills, allowing users to build and visualize models more intuitively and to gain new insights into exploring their uncertainties.******I am proposing to address fundamental research problems in SBIM, NPR/IVis identified in my previous work on applying interactive modeling and visualization to geoscience; and also inspired by examples of traditional geological sketching, rendering, and illustration practices and examples I observed over the years. I am proposing a research program with three interrelated themes and problems as follows: ******(A) “Enhancing Scientific Observations via Sketching” ***A1. Stepwise observational sketching for conceptual model development of natural structures. Synchronized SBIM and NPR, transferring what we observe without or minimally looking at the sketch while applying gestural drawing and rendering.***A2. Rule-based sketching and modeling of natural structures. Algorithmic rules based on the morphology of natural structures and their interfaces, to ensure ‘realistic' structures are generated during sketch-input and in the model output.***(B) “Visual Abstractions of Natural Materials & Structures” ***B1. Interactive non-photorealistic rendering of natural materials. Interactive procedural NPR of fabric patterns rendered in varying multi-scale geometries and structural relationships.***B2. Interactive illustrative visualization of complex natural structures. Higher-level rendering abstraction inspired by eight general scientific illustration techniques and specific examples used in geo-scientific illustrations.***(C) “Similarity-based Conceptual Sketching & Rendering.” Integrated set of data and visual analytics algorithms to classify existing sketches and renderings and generate new ones based on similarity of multi-dimensional attributes from the input models.******The research outcomes are extendable to a broader number of scientific and engineering disciplines and applications requiring conceptual sketching and rendering in the style of our geology-inspired solutions. **

*在任何建模活动的早期阶段，用户都会协作构建一系列概念模型，这些模型有助于形成想法、建议可能的解决方案，并做出影响后续建模阶段的重要决策。概念模型通常表示为手工绘制的草图和效果图，为探索不确定性提供了丰富的视觉参考，并有助于描述更具解释性、更接近目标模型的版本。在这一过程中提出的帮助用户的计算工具的基础是基于草图的界面和建模(SBIM)、非照片真实感绘制(NPR)和说明性可视化(IVIS)等计算机图形学和可视化领域。这些领域引入了一种新的范例，它利用了传统插画家的基本和应用的绘图和渲染技能，允许用户更直观地构建和可视化模型，并获得探索其不确定性的新见解。*我建议解决SBIM、NPR/IVIS中的基础研究问题，这些问题是在我之前将交互式建模和可视化应用于地学的工作中发现的；也受到传统地质草图、渲染和插图实践的例子和我多年来观察到的例子的启发。我提出了一个研究计划，有三个相互关联的主题和问题如下：*(A)“通过素描加强科学观察”*A1。自然结构概念模型开发的逐步观察性草图绘制。同步SBIM和NPR，在应用手势绘制和渲染时，无需或最少查看草图即可传输我们观察到的内容。*A2。自然结构的基于规则的草图绘制和建模。基于自然结构及其界面的形态的算法规则，以确保在草图输入和模型输出中生成真实的结构。*(B)《自然材料和结构的视觉抽象》*B1。自然材质的交互式非照片级真实感渲染。在不同的多尺度几何形状和结构关系中呈现的织物图案的交互式程序NPR。*B2。复杂自然结构的交互式图解可视化。更高层次的绘制抽象，灵感来自于地学插图中使用的八种一般科学插图技术和具体实例。*(C)“基于相似性的概念草图和绘制”。集成了一套数据和视觉分析算法，可对现有草图和效果图进行分类，并根据输入模型中多维属性的相似性生成新的效果图。*研究成果可扩展到更广泛的科学和工程学科，以及需要以地质学为灵感的解决方案风格的概念性草图和效果图的应用。**