Matt Leotta, Ph.D.

Assistant Director of Computer Vision

Computer Vision

Kitware New York
Clifton Park, NY

10 Years Service at Kitware

Ph.D. in Computer Engineering
Brown University

M.S. in Applied Mathematics
Brown University

B.S. in Computer Science and Computer Systems Engineering
Rensselaer Polytechnic Institute

Matt Leotta

Matt Leotta, Ph.D. is an assistant director of computer vision at Kitware and is located in Clifton Park, New York. The projects he leads primarily focus on 3D reconstruction from imagery and video. However, Matt has also contributed to super-resolution, object detection, and tracking research projects. He has received funding from various government agencies and commercial organizations.

Matt is the principal investigator (PI) on Kitware’s team for the Intelligence Advanced Research Projects Activity (IARPA) SMART program. As PI he leads a Kitware team along with four universities and two outside companies to develop a solution for the detection and characterization of man-made change, such as heavy construction, using various sources of satellite imagery that must be harmonized together.  Previously, Matt was also the PI on Kitware’s team for the IARPA CORE3D program. For that program, he led three universities and two outside companies to develop Danesfield, an open source framework for 3D semantic reconstruction of buildings from satellite imagery.

Matt is the founder and lead maintainer of TeleSculptor, Kitware’s open source desktop application for 3D reconstruction from aerial video. He is also one of the founders and maintainers of the KWIVER toolkit on which TeleSculptor and other applications were built. This 3D computer vision software is the culmination of work on several Small Business Innovation Research (SBIR) projects for which Matt served as PI or has mentored the PI. The initial work started with an SBIR project with Air Force Research Laboratory (AFRL) in 2013 and has since been extended with additional SBIR funding from AFRL, Army Night Vision, and Electronic Sensors Directorate (NVESD), United States Special Operations Command (SOCOM), and the National Geospatial-Intelligence Agency (NGA).

Matt led a commercial effort to develop algorithms for visual navigation of an endoscope for medical applications that resulted in US Patent 10169875.

In addition to his research projects, Matt is also involved in recruiting and interviewing for Kitware’s Open Source Software Technology Program (OSTP), computer vision interns, and full-time employees. He also mentors students during their internship at Kitware.

Matt received his Ph.D. in computer engineering from Brown University in 2010. Under the supervision of Professor Joseph Mundy, Matt’s work focused on tracking vehicles in traffic videos while simultaneously reconstructing 3D models of the vehicles by fitting a generic deformable model. In 2007, Matt also received his master’s degree in applied mathematics from Brown. He received his bachelor’s degree in computer science and computer systems engineering from Rensselaer Polytechnic Institute in 2003. He graduated summa cum laude. During his graduate and undergraduate studies, Matt worked as a research assistant in robotics and computer vision.

Awards

  • Best Paper Award presented by EarthVision Workshop, CVPR, 2019

    M. Leotta, C. Long, B. Jacquet, M. Zins, D. Lipsa, J. Shan, B. Xu, Z. Li, X. Zhang, S. Chang, M. Purri, J. Xue, and K. Dana, “Urban Semantic 3D Reconstruction From Multiview Satellite Imagery,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops: EarthVision, 2019. [URL]

Invited Talks & Media

  • Keynote Speaker, 10th International Workshop on Pattern Recognition in Remote Sensing (PRRS), 2018

  • Organizer and presenter, Open source structure-from-motion, CVPR, 2015

  • Organizer and presenter, Open source computer vision using Python, CVPR, 2012

Professional Associations & Service

  • Current member of the Computer Vision Foundation (CVF)

  • Program committee member for The IEEE Winter Conference on Applications of Computer Vision (WACV), 2013-2017, 2022

  • Program committee member for CVPR, 2012-2021

  • Program committee member for The Association for the Advancement of Artificial Intelligence (AAAI), 2020-2022

  • Program committee member for The EarthVision Workshop at CVPR, 2020-2021

  • Program committee member for The International Conference on Computer Vision (ICCV), 2013, 2015, 2017, 2021

  • North America corporate relations chair for the International Conference on Pattern Recognition (ICPR), 2020

  • Program committee member for The British Machine Vision Conference (BMVC), 2017-2020

  • Program committee member for The European Conference on Computer Vision (ECCV), 2012, 2016, 2020

  • Program committee member for The Asian Conference on Computer Vision (ACCV), 2014, 2016, 2018

Publications

  1. M. Leotta, D. Russell, and A. Matrai, "On the Maximum Radius of Polynomial Lens Distortion," in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022. [URL]
  2. M. Leotta, J. Shan, X. Zhang, C. Long, B. Xu, M. Purri, M. Zins, B. Jacquet, K. Dana, S. Seida, M. Berlin, Z. Li, J. Xue, and D. Lipsa, "Danesfield: Integrating Deep Learning and Classical Methods for Multiview Semantic 3D Modeling," in Proceedings of the MSS National Symposium on Sensor and Data Fusion, 2019.
  3. M. Leotta, C. Long, B. Jacquet, M. Zins, D. Lipsa, J. Shan, B. Xu, Z. Li, X. Zhang, S. Chang, M. Purri, J. Xue, and K. Dana, "Urban Semantic 3D Reconstruction From Multiview Satellite Imagery," in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops: EarthVision, 2019. Winner, Best Paper Award. [URL]
  4. M. Leotta, E. Smith, and D. Russell, "TeleSculptor: Dense 3D Models from Uncalibrated FMV," in Proceedings of the MSS National Symposium on Passive Sensors, 2018.
  5. M. Leotta, E. Smith, M. Dawkins, and P. Tunison, "Open source structure-from-motion for aerial video," in Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2016. [URL]
  6. M. Leotta, P. Tunison, E. Smith, and M. Dawkins, "MAP-Tk: Motion imagery Aerial Photogrammetry Toolkit," in Proceedings of the MSS National Symposium on Passive Sensors, 2015.
  7. K. Fieldhouse, M. Leotta, A. Basharat, R. Blue, D. Stoup, C. Atkins, L. Sherrill, B. Boeckel, P. Tunison, J. Becker, M. Dawkins, M. Woehlke, R. Collins, M. Turek, and A. Hoogs, "KWIVER: An open source cross-platform video exploitation framework," in Proceedings of the IEEE Applied Imagery Pattern Recognition Workshop, 2014. [URL]
  8. Z. Sun, M. Leotta, A. Hoogs, R. Blue, R. Neuroth, J. Vasquez, A. Perera, M. Turek, and E. Blasch, "Vehicle change detection from aerial imagery using detection response maps," in SPIE Defense, Security, and Sensing Motion Imagery for ISR and Situational Awareness, 2014. [URL]
  9. M. Leotta and J. Mundy, "Vehicle surveillance with a generic, adaptive, 3D vehicle model," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 7, pp. 1457-1469, Jul. 2011. [URL]
  10. A. Perera, S. Oh, M. Leotta, I. Kim, B. Byun, C. Lee, S. McCloskey, B. Miller, Z. Huang, A. Vahdat, W. Yang, G. Mori, K. Tang, D. Koller, L. Fei-Fei, K. Li, G. Chen, J. Corso, Y. Fu, R. Srihari, Y. Fu, and R. Srihari, "GENIE TRECVID 2011 Multimedia Event Detection : Late-Fusion Approaches to Combine Multiple Audio-Visual features," in NIST TRECVID Workshop, 2011.
  11. M. Leotta, "Generic, deformable models for 3-d vehicle surveillance," Brown University, 2010.
  12. M. Leotta and J. Mundy, "Predicting high resolution image edges with a generic, adaptive, 3-D vehicle model," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2009. [URL]
  13. C. Tsai, B. Madore, M. Leotta, M. Sofka, G. Yang, A. Majerovics, H. Tanenbaum, C. Stewart, and B. Roysam, "Automated retinal image analysis over the internet," IEEE Transactions on Information Technology in Biomedicine, vol. 12, no. 4, pp. 480-487, Jul. 2008. [URL]
  14. M. Leotta, A. Vandergon, and G. Taubin, "3D slit scanning with planar constraints," Computer Graphics Forum, vol. 27, no. 8, pp. 2066-2080, Dec. 2008. [URL]
  15. M. Leotta, A. Vandergon, and G. Taubin, "Interactive 3D ScanningWithout Tracking," in Brazilian Symposium on Computer Graphics and Image Processing, 2007. [URL]
  16. M. Leotta and J. Mundy, "Epipolar curve tracking in 3-D," in Proceedings of the IEEE International Conference on Image Processing, 2007. [URL]
  17. M. Leotta and J. Mundy, "Learning background and shadow appearance with 3-D vehicle models," in Proceedings of the British Machine Vision Conference, 2006. [URL]
  18. D. Han, M. Leotta, D. Cooper, and J. Mundy, "Vehicle class recognition from video-based on 3D curve probes," in IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 2005. [URL]

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