Rate My Professor Ludovic Magerand

Dr Ludovic Magerand serves as a Lecturer (Teaching and Research) in the Department of Computing within the School of Science and Engineering at the University of Dundee, a position he has held since 2019. He obtained his Doctor of Science degree in Dynamic pose estimation with CMOS cameras using sequential acquisition from Université Clermont Auvergne between 2008 and 2014. His academic interests center on computer vision, with a specialization in 3D vision for extracting three-dimensional information from images and related sensors, especially in challenging conditions like dynamic and deformable environments. Magerand is a member of the Computer Vision and Image Processing research group and contributes to the UN Sustainable Development Goal 3: Good Health and Well-being through his work.

Magerand's research applies 3D vision to healthcare, including contributions to the SoftEn project from 2021 to 2023 on 3D localization and depth estimation for autonomous robot navigation in colonoscopy, presented at a MICCAI workshop in 2022. He leads the 3DFace@Home project, active from 2024 to 2026 and funded by UKRI grant EP/X036642/1 as a New Investigator Award, focusing on facial 3D reconstruction from mobile app selfies for dental growth monitoring in collaboration with the University of Glasgow School of Dentistry. He supervises one PhD student as main supervisor and two as second supervisor, with one PhD graduate under his second supervision. In teaching, he delivers modules on operating systems, networks, and artificial intelligence. He has also supervised the creation of the MASIVE dataset for fingerprint verification in 2024.

Magerand has authored key publications including “Revisiting Projective Structure from Motion: A Robust and Efficient Incremental Solution” (IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020), “Practical Projective Structure from Motion (P2SfM)” (IEEE International Conference on Computer Vision, 2017), “Global Optimization of Object Pose and Motion from a Single Rolling Shutter Image with Automatic 2D-3D Matching” (European Conference on Computer Vision, 2012), “SoftEnNet: Symbiotic Monocular Depth Estimation and Lumen Segmentation for Colonoscopy Endorobots” (2023), and “A review on model-based and model-free approaches to control soft actuators and their potentials in colonoscopy” (Frontiers in Robotics and AI, 2023). His research demonstrates impact in computer vision and biomedical imaging.