Surface Visibility for Large Model Visualization

大型模型可视化的表面可见性

• 批准号: 0083836
• 负责人: Greg Turk
• 金额: $ 24.13万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-10-01 至 2003-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0083836&HistoricalAwards=false
• 关键词: Surface; Visibility; Large; Model; Visualization

Many uses of computer visualization rely on displaying surfaces so that a person can easily understand the relationships between the different parts of the visualized object. This project will study new methods to identify the visible parts of an object, and will use this information to produce better images (that is, more accurate and complete images) of scientific data. Some possible applications of this work are medical visualization (e.g. showing the chambers of the heart), studying fracture mechanics (e.g. identifying interior cracks), and computational fluid dynamics (e.g. finding and studying vortices in turbulent flow).Technically, the project has two sub-goals. First, it will automatically identify interior structures of a complex surface, that is, those regions that are not visible from locations outside the object. Currently, this is done manually or by connected component analysis, both of which have limitations. This project will explore both analytical and sampling approaches to quickly computing a visibility function for all surface points. Secondly, the project will automatically produce a group of viewpoints and associated images (an image coverage) that collectively show every visible portion of a given surface. This can be used to both save the user from the error-prone task of manually rotating the object to see all visible portions, and may aid in creating simplified models for rapid navigation and display. This project will explore several approaches, including sampling-based ones, to this previously unsolved problem.

计算机可视化的许多用途依赖于显示表面，使得人们可以容易地理解可视化对象的不同部分之间的关系。该项目将研究识别物体可见部分的新方法，并将利用这些信息制作更好的科学数据图像（即更准确和完整的图像）。这项工作的一些可能的应用是医学可视化（例如显示心脏的腔室），研究断裂力学（例如识别内部裂缝）和计算流体动力学（例如发现和研究湍流中的涡流）。首先，它将自动识别复杂表面的内部结构，即从对象外部不可见的区域。目前，这是手动或通过连通分量分析来完成的，这两种方法都有局限性。这个项目将探索分析和采样方法，以快速计算所有表面点的可见性函数。其次，该项目将自动生成一组视点和相关图像（图像覆盖范围），共同显示给定表面的每个可见部分。这可以用于将用户从手动旋转对象以查看所有可见部分的容易出错的任务中拯救出来，并且可以帮助创建用于快速导航和显示的简化模型。这个项目将探索几种方法，包括基于采样的方法，来解决这个以前未解决的问题。