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

添加剂制造(AM)的快速发展提高了使用个人打印机制造产品的可获得性、可定制化和可负担性。设计可以由消费者开发，如果他们有足够的机械设计和3D建模知识，或者他们可以从第三方获得。然而，将设计转换为可由3D打印机成功执行的程序的过程通常需要专业的领域知识，而许多最终用户目前缺乏这方面的知识。与此同时，许多物品可能与非技术用户的设计和打印目标非常相似或相同，它们是由行业专家生产的，因此，已经存在数百万经过验证的部件设计。本研究旨在填补上述空白，为3D模型开发一个理论上合理且可实际部署的、特定于领域的在线搜索引擎，称为Srch3D。Srch3D将为非技术终端用户提供用户友好的解决方案，以便在现有经过验证的部件设计的大型存储库中高效地搜索他们的部件。该项目的成果将包括高级3D模型分析、索引和搜索算法，这些算法可以在运行时准确地在大量经过验证的设计文件中识别感兴趣的设计。这项研究将涉及通过具有自适应分辨率的3D对象检测来开发自动设计搜索的算法。它们将建立在最先进的计算机视觉技术，即梯度直方图(HOG)的基础上，并将其扩展到三维空间，用于制造设计文件。此外，该项目还将研究通过数据驱动建模进行运行时3D对象分类和标记的算法。这些解决方案将使用深度神经网络从大型设计库中搜索和识别感兴趣的对象。使用相对高级的数据驱动模型，以及详细的基于HOG的解决方案，将使我们的在线3D模型搜索引擎能够接受来自用户的各种不同的输入对象格式，例如感兴趣对象的草图或照片、其(部分)G代码、计算机辅助设计设计文件或英文描述和关键字。该框架将通过基于公共云的3D模型搜索服务进行访问。按照Google.com和Virustotal.com分别用于文档和恶意软件搜索的思路，该框架将把上述模块实现为基于云的搜索引擎服务，允许任何人使用不同的输入格式搜索他们感兴趣的设计。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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 be 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.