CPS: Medium: Collaborative Research: Srch3D: Efficient 3D Model Search via Online Manufacturing-specific Object Recognition and Automated Deep Learning-Based Design Classification

CPS：中：协作研究：Srch3D：通过在线制造特定对象识别和基于自动化深度学习的设计分类进行高效 3D 模型搜索

• 批准号: 2240733
• 负责人: Saman Zonouz
• 金额: $ 59.5万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2240733&HistoricalAwards=false
• 关键词: CPS; Medium; Collaborative; Research; Srch3D

Rapid growth in additive manufacturing (AM) has improved the accessibility, customizability and affordability of making products using personal printers. Designs can be developed by consumers, if they have enough knowledge of mechanical design and 3D modeling, or they can be obtained from third parties. However, the process of translating a design to a program that can successfully executed by a 3D printer often requires specialized domain knowledge that many end-users currently lack. In the meantime, lots of objects, which may be very similar or identical to what the non-technical user aims to design and print, have been produced by experts in industry and, hence, millions of proven part designs already exist. This research aims to fill the above-mentioned gap by developing a theoretically sound and practically deployable, domain-specific online search engine, called Srch3D, for 3D models. Srch3D will provide the non-technical end-users with a user-friendly solution to efficiently search for their components in a large repository of existing proven part designs. The outcomes of this project will include algorithms for advanced 3D model analysis, indexing and search algorithms that can identify designs of interest within a large number of proven design files accurately in runtime. The research will involve development of algorithms for automated design search via 3D object detection with adaptive resolutions. They will build on top of state-of-the-art computer vision techniques, namely histogram of gradients (HOG), and extend them to three-dimensional spaces for the manufacturing design files. Additionally, the project will research algorithms for runtime 3D object classification and labeling via data-driven modeling. The solutions will use deep neural networks to search and identify objects of interest from a large design repository. The use of relatively high-level data-driven models, along with the detailed HOG-based solutions, will enable our online 3D model search engine to accept a different variety of input object formats from the users, such as sketches or photos of the objects of interest, their (partial) G-Code, computer-aided design design files, or English descriptions and keywords. The framework will be accessible via a public cloud-based 3D model search service. In the vein of google.com and virustotal.com for document and malware search, respectively, the framework will realize the aforementioned modules as a cloud-based search engine service that allows anyone to search for their design of interest using different input formats.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.

添加剂制造（AM）的快速增长改善了使用个人打印机生产产品的可访问性，可定制性和负担能力。 如果消费者对机械设计和3D建模有足够的了解，则可以开发设计，或者可以从第三方获得。但是，将设计转换为可以通过3D打印机成功执行的程序的过程通常需要许多最终用户目前缺乏的专门域知识。同时，许多对象可能与非技术用户旨在设计和印刷的目标非常相似或相同，因此已经由行业专家生产，因此已经存在数百万个已验证的部分设计。这项研究旨在通过为3D模型开发理论上声音和可部署的，特定于域的在线搜索引擎（称为SRCH3D）来填补上述空白。 SRCH3D将为非技术最终用户提供用户友好的解决方案，以在现有经过验证的零件设计的大量存储库中有效地搜索其组件。该项目的结果将包括用于高级3D模型分析，索引和搜索算法的算法，这些算法可以在运行时准确地确定大量已验证的设计文件中感兴趣的设计。该研究将涉及通过3D对象检测的自动设计搜索算法的开发，并具有自适应分辨率。它们将建立在最先进的计算机视觉技术的基础上，即梯度（HOG）的直方图，并将其扩展到制造设计文件的三维空间。此外，该项目将研究用于运行时3D对象分类和通过数据驱动建模标记的算法。解决方案将使用深层神经网络从大型设计存储库中搜索和识别感兴趣的对象。使用相对较高的数据驱动模型以及基于详细的HOG解决方案的使用将使我们的在线3D模型搜索引擎能够接受用户的不同类型的输入对象格式，例如草图或感兴趣的对象的照片，其（部分）G代码，其（部分）G-Code，计算机辅助，计算机辅助的，计算机辅助的设计设计文件或英语描述和英语描述和关键字。该框架将通过基于公共云的3D模型搜索服务访问。在Google.com和virustotal.com的文档和恶意软件搜索中，该框架将实现上述模块作为基于云的搜索引擎服务，使任何人都可以使用不同的输入格式来搜索他们感兴趣的设计。该奖项反映了NSF的法规任务，并被认为是通过基金会的知识优点和广泛的评估来评估的，并且值得通过评估来进行评估。