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.

该I-Corps项目的更广泛的影响/商业潜力是开发针对新手用户的交互式3D扫描工具。 提出的技术的目的是通过3D扫描仪桥接3D重建，通常需要广泛的专业知识，向没有几何处理背景的新手用户。 3D扫描仪生成深度扫描，这是基础对象的部分表面。 3D重建通常需要在不同的相机姿势（称为注册程序）下注册深度扫描，并从注册扫描（称为表面重建）中重建完整的表面。注册的性能在很大程度上取决于扫描如何相互重叠的结构。 提出的技术使用原则上的不确定性量化框架，可以准确预测深度扫描中3D重建的不确定性。据确定，预测的不确定性提供了一种有效的手段来指导扫描计划，以最大程度地减少扫描数量并改善重建质量。 该技术可能会减少3D获取时间并提高重建质量。应用包括牙医进行手术计划的人牙的重建，建筑物的重建，桥梁和其他建筑文物进行损害检查，3D交通交通事故的重建以及用于模型检查的机械零件的3D重建。该I-Corps项目基于开发用于多扫描注册和重建的不确定性定量算法，并根据量化的不确定性查看计划算法。拟议的技术旨在量化当前3D重建，自动化重建完整性检查的不确定性，在线扫描计划，以减少重建的不确定性，增强完整性并最大程度地减少扫描时间。目的是将拟议的技术集成到现有的3D重建软件包中，使其易于用于没有几何处理背景的新手用户。拟议的技术解决了现有的基于扫描的3D重建技术的局限性，尤其是漫长的获取期以及缺乏有效的重建质量评估方法，这些方法在研究文献中已有证明是有充分记录的问题。研究拟议技术的研究发现表明，有效的不确定性量化技术可以提高3D重建的质量并减少扫描时间。该奖项反映了NSF的法定任务，并且认为值得通过基金会的知识分子优点和更广泛的影响审查标准通过评估来获得支持。