William Guimont-Martin is currently a Ph.D candidate in Norlab laboratory supervised by Prof. Philippe Giguère at Université Laval, and he is a member of Norlab. He graduated from Université Laval with bachelor’s degree in Software Engineering, Distinction Profile, in 2021. During his undergraduate years, William gained valuable research experience through internships in various settings. Notably, he contributed to research on ultrasound data analysis and computer vision during an internship at Olympus NDT Canada. His work at Bentley Systems even led to a US patent, a testament to his ability to innovate. In the summer of 2020, William completed a research internship in Norlab, where he embarked on a journey to explore the application of deep learning to 3D point clouds. This pivotal experience ignited his passion for this emerging field, shaping the trajectory of his academic pursuits. William’s main research interests include deep learning, point clouds, LiDAR and mobile robotics.

Education

• M.Sc. in Computer Sciences at Université Laval (fast-track to PhD), 2023
• B.Ing. in Software Engineering, Distinction Profile, at Université Laval, 2021

Implication

Since 2016, William has been a mentor in a FIRST Robotics team, Les Chevaliers 5440 from École secondaire de la Seigneurie. He teaches the basics of robotics and programming to secondary school students.

Publications

Conference Articles

1. LaRocque, D., Guimont-Martin, W., Duclos, D.-A., Giguère, P., & Pomerleau, F. (2024). Proprioception Is All You Need: Terrain Classification for Boreal Forests. ArXiv Preprint ArXiv:2403.16877, Accepted to the 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). https://doi.org/10.48550/arXiv.2403.16877 Accepted
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2. Guimont-Martin, W., Fortin, J.-M., Pomerleau, F., & Giguère, P. (2023). MaskBEV: Joint Object Detection and Footprint Completion for Bird’s-eye View 3D Point Clouds. Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS). Accepted for oral presentation
    Bibtex source

Miscellaneous

1. Guimont-Martin, W., Fortin, J.-M., Pomerleau, F., & Giguère, P. (2023). MaskBEV: Joint Object Detection and Footprint Completion for Bird’s-eye View 3D Point Clouds. Colloque REPARTI, Université Laval.
    Bibtex source