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ABOUT TARS

DIRECTORS

Natasha Banerjee
Associate Professor
Computer Science
CV | Scholar
Sean Banerjee
Associate Professor
Computer Science
CV | Scholar

At the Terascale All-sensing Research Studio (TARS) at Clarkson University, we perform research in human-driven artificial intelligence using capture and analysis of dense multi-person interactions in online and real-world environments. Our studio houses two multi-modal sensing spaces---the Gazebo, a dense multi-modal sensing space consisting of 16 Kinect sensors, 192 226-FPS color cameras, 16 thermal cameras, 16 sEMG sensors, 16 AMD Ryzen machines each with a 1080 Ti GPU, and 24 Intel 6-core machines to study multi-person interactions, and the Cube, a space with 4 Kinects, 5 thermal cameras, and 4 226-FPS color cameras to study two-person interactions. It also houses two Kinova Gen3 robotic manipulators, one LoCoBot mobile robot manipulator, multiple lab-built drones, a lab-built walker robot, and several VR systems, 3D printers, 3D scanners, a compute cluster with 275,000+ CUDA cores and 4,800+ Tensor cores spread over 50+ GPUs, and a 2+ petabyte data center. The core of our work is in understanding how humans perform tasks in the real-world, to inform next generation artificial intelligence (AI), robotics, and virtual reality. Our research spans computer vision, deep learning, computer graphics, and human-computer interaction. The studio is funded through multiple NSF grants.

We look for graduate and undergraduate students with excellent academic credentials, substantial project work in computer science and AI (at the graduate level) and a strong drive to perform research with us. Contact either Dr. Sean Banerjee (sbanerje a clarkson d edu) or Dr. Natasha Banerjee (nbanerje a clarkson d edu) to determine research opportunities with the studio, or to schedule a visit.

SELECT PUBLICATIONS

N. Wiederhold, A. Megyeri, D. Paris, S. Banerjee N. K. Banerjee (2023). HOH: Markerless Multimodal Human-Object-Human Handover Dataset with Large Object Count. Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track, New Orleans, Louisiana.

[Webpage]  [Paper]

N. Lamb, B. Molloy, C. Palmer, S. Banerjee, and N. K. Banerjee (2023). Fantastic Breaks: A Dataset of Paired 3D Scans of Real-World Broken Objects and Their Complete Counterparts. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, Canada.

[Webpage]  [Paper]  [Video]

N. Wiederhold, M. Li, N. Lamb, D. Paris, A. Tulskie, S. Banerjee, and N. K. Banerjee (2023). Studying How Object Handoff Orientations Relate to Subject Preferences on Handover. IEEE International Conference on Advanced Robotics and its Social Impacts (ARSO), Berlin, Germany.

[Paper]  [Video]

G. Kaur, S. Banerjee, and N. K. Banerjee (2023). Perception of Human-Robot Collaboration Across Countries and Job Domains. IEEE International Conference on Advanced Robotics and its Social Impacts (ARSO), Berlin, Germany.

[Paper]

G. Kaur, S. Banerjee, and N. K. Banerjee (2023). Studying Worker Perceptions on Safety, Autonomy, and Job Security in Human-Robot Collaboration. International Conference on Automation, Robotics and Applications (ICARA), Abu Dhabi, UAE

[Paper]

X. Song, N. Lamb, S. Banerjee, and N. K. Banerjee (2023). Reinforcement-Learning Based Robotic Assembly of Fractured Objects Using Visual and Tactile Information. International Conference on Automation, Robotics and Applications (ICARA), Abu Dhabi, UAE

[Paper]

N. Lamb, S. Banerjee, and N. K. Banerjee (2022). DeepJoin: Learning a Joint Occupancy, Signed Distance, and Normal Field Function for Shape Repair. SIGGRAPH Asia.

[Paper]  [Video]  [Code]

N. Lamb, S. Banerjee, and N. K. Banerjee (2022). DeepMend: Learning Occupancy Functions to Represent Shape for Repair. European Conference on Computer Vision (ECCV).

[Paper]  [Video]  [Code]

N. Lamb, S. Banerjee, and N. K. Banerjee (2022). MendNet: Restoration of Fractured Shapes Using Learned Occupancy Functions. Computer Graphics Forum (CGF, Proc. Symposium on Computer Graphics, SGP).

[Paper]  [Video]  [Code]

J. Judge, P. R. K. Prosun, O. Talmage, A. Dykeman, S. Banerjee, and N. K. Banerjee (2022). Predicting Weight and Strenuousness from High-Speed Videos of Subjects Attempting Lift. IEEE Conference on Connected Health: Applications, Systems, and Engineering Technologies (CHASE).

[Paper]

A. Dykeman, J. Judge, P. R. K. Prosun, G. Kaur, O. Talmage, S. Banerjee, and N. K. Banerjee (2022). Post-Lift Analysis of Thermal Imprint for Weight and Effort Detection. IEEE Conference on Connected Health: Applications, Systems, and Engineering Technologies (CHASE).

[Paper]

R. Turner, N. K. Banerjee, and S. Banerjee (2022). Using Video Motion Vectors for Structure from Motion 3D Reconstruction. International Conference on Signal Processing and Multimedia Applications (SIGMAP).

[Paper]

R. Miller, N. K. Banerjee, and S. Banerjee (2022). Combining Real-World Constraints on User Behavior with Deep Neural Networks for Virtual Reality (VR) Biometrics. IEEE Conference on Virtual Reality and 3D User Interfaces (IEEE VR).

[Paper]  [Video]  [Code]

R. Miller, N. K. Banerjee, and S. Banerjee (2022). Temporal Effects in Motion Behavior for Virtual Reality (VR) Biometrics. IEEE Conference on Virtual Reality and 3D User Interfaces (IEEE VR).

[Paper]  [Video]  [Code]

R. Miller, N. K. Banerjee, and S. Banerjee (2022). Using External Video to Attack Behavior-Based Security Mechanisms in Virtual Reality (VR). IEEE Conference on Virtual Reality and 3D User Interfaces (IEEE VR).

[Paper]  [Video]  [Code]

N. Lamb, N. Wiederhold, B. Lamb, S. Banerjee, and N. K. Banerjee (2021). Using Learned Visual and Geometric Features to Retrieve Complete 3D Proxies for Broken Objects. ACM Symposium on Computational Fabrication (SCF).

[Paper]  [Code]

R. Miller, N. K. Banerjee, and S. Banerjee (2021). Using Siamese Neural Networks to Perform Cross-System Behavioral Authentication in Virtual Reality. IEEE Conference on Virtual Reality and 3D User Interfaces (IEEE VR).

[Paper]

R. Miller, N. K. Banerjee, and S. Banerjee (2020). Within-System and Cross-System Behavior-Based Biometric Authentication in Virtual Reality. IEEE Conference on Virtual Reality and 3D User Interfaces Workshops (VRW), Atlanta, Georgia.

[Paper]

J. Gately, Y. Liang, M. K. Wright, N. K. Banerjee, S. Banerjee, and S. Dey (2020). Automatic Material Classification Using Thermal Finger Impression. International Conference on Multimedia Modeling (MMM), Daejeon, Korea.

[Paper]

R. Miller, A. Ajit, N. K. Banerjee, and S. Banerjee (2019). Realtime Behavior-Based Continual Authentication of Users in Virtual Reality Environments. IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR), San Diego, California.

[Paper]  [Video]

D. Gwinn, J. Helmick, N. K. Banerjee, and S. Banerjee (2019). Comparison of Traditional and Constrained Recursive Clustering Approaches for Generating Optimal Census Block Group Clusters. Communications in Computer and Information Science.

[Paper]

A. Ajit, N. K. Banerjee, and S. Banerjee (2019). Combining Pairwise Feature Matches from Device Trajectories for Biometric Authentication in Virtual Reality Environments. IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR), San Diego, California [Best Student Paper].

[Paper]

N. Lamb, S. Banerjee, and N. K. Banerjee (2019). Automated Reconstruction of Smoothly Joining 3D Printed Restorations to Fix Broken Objects. ACM Symposium on Computational Fabrication (SCF), Pittsburgh, Pennsylvania.

[Paper]  [Presentation]  [Video]

Y. Jiang, S. Banerjee, and N. K. Banerjee (2019). Predicting Human Grasp Locations on Cup Handles by Using Deep Neural Networks to Infer Heat Signatures from Depth Data. IEEE International Conference on Multimedia and Expo Workshops (ICMEW), Shanghai, China.

[Paper]

J. Ryan, S. Inzerillo, J. Helmick, A. Boolani, N. K. Banerjee, and S. Banerjee (2019). Predicting If Older Adults Perform Cognitive Tasks Using Body Joint Movements From RGB-D Videos. The Eleventh International Conference on eHealth, Telemedicine, and Social Medicine (eTELEMED), Athens, Greece.

[Paper]

A. Kupin, B. Moeller, N. K. Banerjee, and S. Banerjee (2019). Task-Driven Biometric Authentication of Users in Virtual Reality (VR) Environments. International Conference on Multimedia Modeling (MMM), Thessaloniki, Greece [Best Paper Candidate].

[Paper]

Y. Jiang, E. Schenck, S. Kranz, S. Banerjee, and N. K. Banerjee (2019). CNN-Based Non-Contact Detection of Food Level in Bottles from RGB Images. International Conference on Multimedia Modeling (MMM), Thessaloniki, Greece.

[Paper]

Y. Jiang, M. Luo, S. Banerjee, and N. K. Banerjee (2018). When Will Breakfast Be Ready: Temporal Prediction of Food Readiness Using Deep Convolutional Neural Networks on Thermal Videos. IEEE Intl. Conf. on Multimedia and Expo Workshops (ICMEW), San Diego, CA.

[Paper]

T. Dunn, S. Banerjee, and N. K. Banerjee (2018). User-Independent Detection of Swipe Pressure using a Thermal Camera for Natural Surface Interaction. International Workshop on Multimedia Signal Processing (MMSP), Vancouver, Canada [Top 5% Paper Award].

[Paper]

Y. Jiang, D. Russell, T. Godisart, N. K. Banerjee, and S. Banerjee (2018). Hardware Synchronization of Multiple Kinects and Microphones for 3D Audiovisual Spatiotemporal Data Capture. IEEE International Conference on Multimedia and Expo (ICME), San Diego, California.

[Paper]

L. Guo, H. Quant, N. Lamb, B. Lowit, N. K. Banerjee, and S. Banerjee (2018). Multi-Camera Microenvironment to Capture Multi-view Time-lapse Videos for 3D Analysis of Aging Objects. International Conference on Multimedia Modeling (MMM), Bangkok, Thailand.

[Paper]  [Video]

L. Guo, H. Quant, N. Lamb, B. Lowit, S. Banerjee, and N. K. Banerjee (2018). Spatiotemporal 3D Models of Aging Fruit from Multi-View Time-Lapse Videos. International Conference on Multimedia Modeling (MMM), Bangkok, Thailand.

[Paper]

N. Lamb, N. K. Banerjee, and S. Banerjee (2018). Programmatic 3D Printing of a Revolving Camera Track to Automatically Capture Dense Images for 3D Scanning of Objects. Intl. Conf. on Multimedia Modeling (MMM), Bangkok, Thailand.

[Paper]  [Video]

H. Quant, S. Banerjee, and N. K. Banerjee (2018). A virtual reality interface for interactions with spatiotemporal 3D data. International Conference on Multimedia Modeling (MMM), Bangkok, Thailand.

[Paper]  [Video]

D. Gwinn, J. Helmick, N. K. Banerjee, and S. Banerjee (2018). Optimal Estimation of Census Block Group Clusters to Improve the Computational Efficiency of Drive Time Calculations. Intl. Conf. on Geographical Information Systems Theory, Applications and Management (GISTAM), Madeira, Portugal.

[Paper]

T. Dunn, N. K. Banerjee, and S. Banerjee (2016). GPU acceleration of document similarity measures for automated bug triaging. International Workshop on Software Faults (co-located with IEEE International Symposium on Software Reliability Engineering, ISSRE), Ottawa, Canada.

[Paper]

AWARDS

Best Student Paper Runner Up at 2022 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies

Best Paper at 2022 International Conference on Signal Processing and Multimedia Applications

Best Paper Honorable Mention at 2022 IEEE Conference on Virtual Reality and 3D User Interfaces

Best Student Paper at 2019 International Conference on Artificial Intelligence & Virtual Reality

Best Paper Candidate at 2019 International Conference on Multimedia Modeling

Top 5% Paper Award at 2018 Multimedia Signal Processing Workshop

PEOPLE

Natasha Banerjee
Associate Professor
Computer Science
Sean Banerjee
Associate Professor
Computer Science

PhD STUDENTS

Nikolas Lamb
Computer Science
Noah Wiederhold
Computer Science
Xinchao Song
Computer Science
Priyo Ranjan Kundu Prosun
Computer Science
Gurpreet Kaur
Computer Science
Richard Turner
Computer Science
Mingjun Li
Computer Science
Irfan Yaqoob
Computer Science
Numan Zafar
Computer Science
Kun Han
Computer Science
Yu Liu
Computer Science
Sichao Li
Computer Science
Houchao Gan
Computer Science
Yue Pan
Computer Science
Irving Manaog
Computer Science

MS STUDENTS

DiMaggio Paris
Computer Science
Alex Kupin
Computer Science
Alaina Tulskie
Computer Science
Jyothinadh Minnekanti
Computer Science

UNDERGRADUATE STUDENTS

Ava Megyeri
Computer Science
Maryangela White
Computer Science
Ethan Matzek
Computer Science
Tyler Yankee
Computer Science
Odin Kohler
Computer Science
Robert Licata
Computer Science
Niall Pepper
Computer Science
Matt Duver
Computer Science

ALUMNI

Owen Talmage (MS)
Computer Science
Austin Dykeman (MS)
Computer Science
Robert Miller (PhD)
Computer Science
Yijun Jiang (PhD)
Computer Science
Damon Gwinn (MS)
Computer Science
Marc Bishop (MS)
Computer Science
Miao Luo (MS)
Computer Science
Lintao Guo (MS)
Computer Science

Copyright © Natasha Kholgade Banerjee. All rights reserved.