I-Corps: 3D Scanning Tool for Reconstruction Via Uncertainty Quantification

I-Corps：通过不确定性量化进行重建的 3D 扫描工具

• 批准号: 2330157
• 负责人: Qixing Huang
• 金额: $ 5万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2330157&HistoricalAwards=false
• 关键词: Corps; 3D; Scanning; Tool; Reconstruction

The broader impact/commercial potential of this I-Corps project is the development of an interactive 3D scanning tool that targets novice users. The goal of the proposed technology is to bridge 3D reconstruction via 3D scanners, which typically require extensive expertise, to novice users with no background in geometry processing. 3D scanners generate depth scans that are partial surfaces of the underlying object. A 3D reconstruction typically requires registering depth scans under different camera poses, known as the registration procedure, and reconstructing a complete surface from the registered scans, known as surface reconstruction. The performance of registration heavily depends on the structure of how scans overlap with each other. The proposed technology uses a principled uncertainty quantification framework that may accurately predict the uncertainties of 3D reconstruction from depth scans. It was determined that the predicted uncertainties provide an effective means to guide scan planning that minimizes the number of scans and improves reconstruction quality. This technology may reduce the 3D acquisition time and improve the reconstruction quality. Applications include reconstructions of human teeth for dentists to perform surgery planning, reconstructions of buildings, bridges, and other architectural artifacts for damage inspection, 3D reconstructions of traffic accidents for crime analysis, and 3D reconstructions of mechanical parts for model checking. This I-Corps project is based on the development of uncertainty quantification algorithms for multi-scan registration and reconstruction and view planning algorithms based on quantified uncertainties. The proposed technology is designed for quantifying the uncertainties of the current 3D reconstruction, automating reconstruction completeness checking, online scan planning to reduce the uncertainties of reconstruction, enhancing completeness, and minimizing scanning time. The goal is to integrate the proposed technology into existing software packages for 3D reconstruction, making them easy to use for novice users with no background in geometry processing. The proposed technology addresses the limitations of existing scan-based 3D reconstruction techniques, especially the long acquisition period and the lack of effective reconstruction quality assessment methods, problems that have been well-documented in the research literature. Research findings examining the proposed technology suggest that efficient uncertainty quantification techniques may enhance the quality of 3D reconstruction and reduce scanning time.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个I-Corps项目更广泛的影响/商业潜力是开发针对新手用户的交互式3D扫描工具。 所提出的技术的目标是通过3D扫描仪（通常需要广泛的专业知识）将3D重建连接到没有几何处理背景的新手用户。3D扫描仪生成深度扫描，这些深度扫描是底层对象的部分表面。3D重建通常需要在不同的相机姿态下配准深度扫描，称为配准过程，并且从配准的扫描重建完整的表面，称为表面重建。配准的性能在很大程度上取决于扫描如何彼此重叠的结构。 所提出的技术使用原则性的不确定性量化框架，其可以准确地预测来自深度扫描的3D重建的不确定性。确定预测的不确定性提供了一种有效的方法来指导扫描规划，最大限度地减少扫描次数并提高重建质量。 该技术可以减少三维采集时间，提高重建质量。应用包括为牙医进行手术规划的人类牙齿重建，用于损坏检查的建筑物，桥梁和其他建筑物的重建，用于犯罪分析的交通事故的3D重建，以及用于模型检查的机械零件的3D重建。这个I-Corps项目是基于多扫描配准和重建的不确定性量化算法和基于量化不确定性的视图规划算法的开发。所提出的技术被设计用于量化当前3D重建的不确定性、自动化重建完整性检查、在线扫描规划以减少重建的不确定性、增强完整性和最小化扫描时间。我们的目标是将所提出的技术集成到现有的三维重建软件包中，使它们易于使用，对于没有几何处理背景的新手用户。所提出的技术解决了现有的基于扫描的3D重建技术的局限性，特别是采集周期长和缺乏有效的重建质量评估方法，这些问题已经在研究文献中得到了很好的证明。研究结果表明，有效的不确定性量化技术可以提高三维重建的质量，减少扫描时间。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。