CGV: Medium: Collaborative Research: A Heterogeneous Inference Framework for 3D Modeling and Rendering of Sites

CGV：媒介：协作研究：用于站点 3D 建模和渲染的异构推理框架

• 批准号: 1302267
• 负责人: Holly Rushmeier
• 金额: $ 60万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1302267&HistoricalAwards=false
• 关键词: CGV; Medium; Collaborative; Research; Heterogeneous

Organizing and using 3D data related to physical sites is important in many applications such as historical reconstruction, architectural design, and urban planning. However, no method has been developed that exploits the full range of data types available for such sites. Useful data often comes from historical sources, and requires substantial processing to be useful. Some of this processing can be automated, but some of it must be done by humans. An as-yet unsolved problem is how to coordinate human effort to efficiently carry out this process. In the current project the PIs will address quantitative and qualitative accuracy issues in reconstructing 3D sites so as to allow for input and participation by different populations in building data sets, and will demonstrate a variety of applications using a heterogeneous 3D site representation. Specifically, the work will make the following contributions: new techniques for annotating heterogeneous input will be developed, balancing automated and human input; new techniques for coordinating digital computation, human computation, and machine learning will be devised; new tools for architectural analysis and design, and for material weathering analysis, will be developed based on the new 3D representation; and new ideas for storytelling from 3D data will be demonstrated. Project outcomes will include a new organization of heterogeneous data for 3D sites, new insights into the relative contributions of automated techniques and human computation in the domain of 3D site data (which will be applicable to other challenging problems involving large complex data sets), new algorithms for reconstructing 3D models, and new techniques for conducting studies in architecture and in cultural heritage.Broader Impacts: This research will have a strong impact on architectural-design and cultural heritage documentation, interpretation and communication. The various phases of the project will involve students at both the graduate and undergraduate levels, and in diverse disciplines including computer science, architecture, and art history. The PIs will produce teaching modules based on this work targeted at computer science, architecture, and cultural heritage.

在许多应用程序（例如历史重建，建筑设计和城市规划）等许多应用中，组织和使用与物理站点有关的3D数据很重要。 但是，尚未开发出利用可用于此类网站的全部数据类型的方法。 有用的数据通常来自历史来源，需要大量处理才有用。 其中一些处理可以自动化，但其中一些必须由人类进行。 尚未解决的问题是如何协调人类努力以有效地执行此过程。 在当前项目中，PI将解决重建3D站点的定量和定性准确性问题，以允许不同人群在构建数据集中的投入和参与，并将使用异质的3D站点表示来证明各种应用程序。具体而言，这项工作将做出以下贡献：将开发出注释异质输入的新技术，平衡自动化和人类的输入；将设计用于协调数字计算，人类计算和机器学习的新技术；将根据新的3D表示制定建筑分析和设计以及物质风化分析的新工具；将展示来自3D数据的讲故事的新想法。 项目结果将包括一个新的3D站点的异质数据组织，对3D站点数据域中自动化技术的相对贡献和人类计算的相对贡献的新组织（这将适用于其他涉及大型复杂数据集的具有挑战性的问题），对重建3D模型和新技术的新算法的新算法，以实现3D模型和新技术，以构建构造和构建构建的研究，并在建筑中构建了构建手术，并在构造中构建了构建手术，并在构造过程中构建了构建手术，并在构造过程中进行了构建的研究。对建筑设计和文化遗产文档，解释和交流的影响。 该项目的各个阶段将涉及研究生和本科级别的学生，以及包括计算机科学，建筑和艺术史在内的各种学科。 PI将根据针对计算机科学，建筑和文化遗产的这项工作来生产教学模块。