DREAM - data driven ReActive modelling

DREAM - 数据驱动的响应式建模

• 批准号: 493625-2016
• 负责人: Wyvill, Brian
• 金额: $ 5.61万
• 依托单位: University of Victoria
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=618838
• 关键词: DREAM; data; driven; ReActive; modelling

In a world where both virtual and digital objects reside, it is important that not just the appearance but the behaviour of a digital model should be able to imitate real objects, react realistically to its surroundings or be programmable to behave as we desire. In this project we will develop a modelling paradigm capable of delivering such a goal. At the core of any virtual environment are the geometric models that represent real objects. In this research project, the first step is to develop a modelling paradigm to produce a reactive model; not a mere visual representation of the object in the scene, but one whose behaviour takes account of its behavioral structure, as well as the world in which it exists. Not only should a model be able to react and actively participate with other virtual elements in an organic and physically realistic fashion but it should also be able to answer self-queries. In the same way a smart-phone is aware of its position, status and is fully connected to the internet, our models could be instrumented with meta-information that would further enrich their interaction capability with the augmented environment. For example, a human brain meta-model could be equipped with short range sensors capable of exactly identifying surgery tools positions during virtual brain biopsy. Our approach is to build upon concepts from implicit modelling, in which models of generic topology and geometry can be represented as iso-surfaces of scalar fields. By designing suitable functions the combination of fields and their gradients can yield complex, reactive models. While retaining the power to represent generic geometry, these volumetric models allow for efficient and scalable tracking.

在一个虚拟和数字对象都存在的世界里，重要的是，数字模型的外观和行为都应该能够模拟真实的对象，对其周围环境做出现实的反应，或者通过编程实现我们想要的行为。在这个项目中，我们将开发一个能够实现这一目标的建模范例。任何虚拟环境的核心都是代表真实对象的几何模型。在这个研究项目中，第一步是开发一个模型范例来产生一个反应性模型；不仅仅是场景中对象的可视表示，而是一个其行为考虑其行为结构以及它所在的世界的模型。模特不仅应该能够以有机和现实的方式与其他虚拟元素做出反应并积极参与，而且还应该能够回答自我询问。就像智能手机知道自己的位置、状态并完全连接到互联网一样，我们的模型可以使用元信息进行测量，这将进一步丰富它们与增强环境的交互能力。例如，人脑元模型可以配备短程传感器，能够在虚拟大脑活组织检查期间准确识别手术工具的位置。我们的方法是建立在隐式建模的概念上，在隐式建模中，通用拓扑和几何模型可以表示为标量场的等值面。通过设计合适的函数，场及其梯度的组合可以产生复杂的反应性模型。在保留表示通用几何图形的能力的同时，这些体积模型允许高效和可伸缩的跟踪。