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Clarkson Computer Science PhD Student Presents Work on AI-Driven Repair of Damaged Objects Using Additive Manufacturing at the Top-Ranking Venue in Geometry Processing


Nikolas Lamb, Natasha and Sean Banerjee

Clarkson Computer Science PhD student and NSF Graduate Research Fellowship awardee Nikolas Lamb presented his research on using techniques from artificial intelligence (AI), particularly computer vision and deep learning, to repair the damaged items at the Eurographics Symposium on Geometry Processing (SGP), the highest ranking venue on geometry processing techniques on July 5. Nikolas was advised on his research by Drs. Natasha Banerjee and Sean Banerjee, both Associate Professors in the Department of Computer Science. Nikolas ’research results from the venue proceedings can be seen as published work in the 2022 Computer Graphics Forum, the leading journal for in -depth technical computer graphics articles. Nikolas’s work is the first from Clarkson to be presented at SGP and can be seen in print in the journal Computer Graphics Forum.

Nikolas’s work gives users a new approach, called MendNet — something HEALINGin the Deep Neural network — which automatically synthesizes additively manufactured repair parts into 3D models of damaged objects. Nikolas ’method for automated 3D repair synthesis is the first of its kind. Prior to Nikolas ’research, if a user’s valuable legacy was broken, with damaged parts damaged that could no longer be repaired, the restoration of the damaged object was an important challenge, because the user must diligently 3D model the complex geometry of the broken part. This is something most users can’t do, and it’s no surprise that a large number of damaged items end up being thrown away, adding environmental waste and affecting sustainability.

Nikolas ’research has played an important role in enhancing Clarkson’s commitment to sustainability, by using AI to automate the repair process, encouraging end users to choose‘ fix ’rather than‘ fix ’. ilis’. Users can now repair damaged items, for example, ceramic items such as expensive dishes with little effort. Nikolas ’automated repair algorithm allows users to scan their broken object and can automatically synthesize the repair part and send the part to a 3D printer. Nikolas ’work takes advantage of the widespread ubiquity of 3D printers and the emergence of 3D printers for materials such as ceramic and wood in the consumer market. Through a combination of AI, computer vision, and in-depth study of the manufacturing process, Nikolas ’work has dramatically transformed the landscape of advanced manufacturing, bringing rapid production within the hand of the average user.

Nikolas’s work has had a broader impact on advancing knowledge in domains such as archeology, anthropology, and paleontology, by providing a user-friendly approach to the restoration of cultural artifacts, damaged objects. fossil specimens, and fragmentary remains, which reduces the busy work for researchers and enables them to focus attention on answering research questions of interest in the domain. The work also has the effect of automating repairs in dentistry and medicine.

Nikolas is a member of the Terascale All-sensing Research Studio (TARS) at Clarkson University. TARS supports the research of 15 graduate students and nearly 20 undergraduate students each semester. TARS has one of the largest high-performance computing facilities in Clarkson, with 275,000+ CUDA cores and 4,800+ Tensor cores spread across 50+ GPUs, and 1 petabyte of (almost full!) Storage. TARS houses the Gazebo, a massively dense multi-viewpoint multi-modal markerless motion capture facility for imaging multi-person interactions with 192 226FPS high-speed cameras, 16 Microsoft Azure Kinect RGB-D sensors, 12 Sierra Olympic Viento-G thermal cameras, and 16 surface electromyography (sEMG) sensors, and the Cube, a single- and two-person 3D imaging facility with 4 high-speed cameras, 4 RGB-D sensors, and 5 thermal camera. TARS conducts research using deep learning to gain an understanding of natural multi-person interaction from large datasets, to enable next-generation technologies, for example, intelligent agents and robots, which are seamlessly integrated into future human environments.

The team thanked the Office of Information Technology for providing access to the ACRES GPU node with 4 V100s containing 20,480 CUDA cores and 2,560 Tensor cores.

Nikolas and the TARS team are looking for your broken items that you want to throw away, for further research. Please drop them a line at lambne@clarkson.edu if there are any damaged items you would like to remove.



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