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

该项目解决了从照片集进行大规模3D建模的三个主要挑战：1)高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。这种方法的好处是双重的。首先，通过增强数据关联，该方法增加了三维模型的范围，因为数据链接更完整。其次，通过开发不仅对输入数据可变性具有鲁棒性（并减轻）的算法，而且还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。本项目具体的数据关联任务包括：位置识别、三维重建的视图规划、特征匹配的在线学习、场景对称下的建模。本课题的具体数据开发任务包括：模型更新与归档、原生分辨率建模、高分辨率动态纹理估计、用户行为挖掘与利用。这项工作能够广泛部署应用程序，其中全自动场景建模从确定结构属性扩展到包括场景和用户控制的图像捕获过程中可观察行为模式的建模。开发的技术具有广泛的应用范围，从虚拟旅游到文化遗产保护，再到灾害响应。

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.