EAGER: Data Association and Exploitation for Large Scale 3D Modeling from Visual Imagery

EAGER：视觉图像大规模 3D 建模的数据关联和开发

• 批准号: 1252921
• 负责人: Jan-Michael Frahm
• 金额: $ 29.65万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1252921&HistoricalAwards=false
• 关键词: EAGER; Data; Association; Exploitation; Large

This project addresses three main challenges in large scale 3D modeling from photocollections: 1) efficient and complete data linkage, 2) Inference and modeling of scene and user dynamics, and 3) development of data adaptive algorithms. These challenges are tackled within the framework of data association and exploitation. The benefits of such an approach are two fold. First, through enhanced data association, the approach increases the scope of 3D models due to more complete data linkage. Second, through the development of algorithms that are not only robust against (and mitigate) input data variability, but also explicitly designed to exploit this diversity and data richness, the approach increases fidelity of 3D models. The specific data association tasks of this project include: location recognition, view planning for 3D reconstruction, online learning for feature matching, and modeling under scene symmetries. The specific data exploitation tasks of this project include: model update and archiving, native resolution modeling, high resolution dynamic texture estimation, and exploring and leveraging user behavior. This work enables the broad deployment of applications where fully automated scene modeling expands from determining structure properties to encompass the modeling of observable behavioral patterns both in the scene and in the user controlled image capture process. The developed technologies have a wide range of applications, from virtual tourism, to cultural heritage preservation, to disaster response.

该项目解决了从照片收集的大规模3D建模的三个主要挑战：1）有效和完整的数据链接，2）场景和用户动态的推理和建模，以及3）数据自适应算法的开发。这些挑战是在数据关联和利用的框架内解决的。这种方法的好处有两方面。首先，通过增强的数据关联，该方法由于更完整的数据链接而增加了3D模型的范围。其次，通过开发不仅对输入数据的可变性具有鲁棒性（并减轻），而且明确设计用于利用这种多样性和数据丰富性的算法，该方法提高了3D模型的保真度。该项目的具体数据关联任务包括：位置识别，三维重建的视图规划，特征匹配的在线学习，以及场景对称性下的建模。该项目的具体数据开发任务包括：模型更新和存档、原生分辨率建模、高分辨率动态纹理估计以及探索和利用用户行为。这项工作使广泛部署的应用程序，其中完全自动化的场景建模从确定结构属性扩展到包括在场景和用户控制的图像捕获过程中的可观察的行为模式的建模。所开发的技术有着广泛的应用，从虚拟旅游到文化遗产保护，再到灾害应对。