Saman Zonouz and Mehdi Javanmard receive NSF Grant for Advanced Manufacturing

ECE Associate Professors Saman Zonouz (PI) and Mehdi Javanmard (co-PI) are the recipients of a new NSF award for the project titled "Srch3D: Efficient 3D Model Search via Online Manufacturing-specific Object Recognition and Automated Deep Learning-Based Design Classification." This is a three-year $1.2M collaborative award led by Rutgers with Georgia Tech. Rutgers' share of this award is $600,000.

Saman and his team will develop 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 and for document and malware search, respectively, the proposed 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. 

Congratulations, Saman and Mehdi!