Rate My Professor Ludovic Magerand

Dr Ludovic Magerand serves as a Lecturer (Teaching and Research) in Computing within the School of Science and Engineering at the University of Dundee. He holds a Doctor of Science degree from Université Clermont Auvergne, awarded in 2014 for his thesis on dynamic pose estimation with CMOS cameras using sequential acquisition. Magerand's research focuses on 3D vision, extracting three-dimensional information from images or sensors, particularly in challenging conditions including dynamic and deformable environments. His primary current line of work applies 3D vision to healthcare purposes, such as autonomous robot navigation in colonoscopy and facial growth monitoring in dentistry. He is part of the Computer Vision and Image Processing research group, contributing to artificial intelligence and machine learning for multi-modal image and video processing tasks including detection, segmentation, recognition, reconstruction, inference, prediction, and assessment. In teaching, he delivers modules related to operating systems, networks, and Artificial Intelligence.

Since arriving at the University of Dundee in 2019, Magerand contributed to the SoftEn project on 3D localization and depth estimation for colonoscopy endorobots, with research presented at the MICCAI ISGIE workshop in 2022. He leads the 3DFace@Home project, funded by an EPSRC New Investigator Award (EP/X036642/1), developing a mobile application and AI system for accurate facial 3D reconstruction from selfies captured at home, in collaboration with the Schools of Dentistry at Dundee and Glasgow. He serves as main supervisor for one PhD student, second supervisor for two others, and one PhD has graduated under his second supervision. Key publications include "Global Optimization of Object Pose and Motion from a Single Rolling Shutter Image with Automatic 2D-3D Matching" (ECCV 2012), "Revisiting Projective Structure from Motion: A Robust and Efficient Incremental Solution" (IEEE TPAMI 2020), "Practical Projective Structure from Motion (P2SfM)" (ICCV 2017), "SoftEnNet: Symbiotic Monocular Depth Estimation and Lumen Segmentation for Colonoscopy Endorobots" (2023), and "Real-Time Lumen Detection for Autonomous Colonoscopy" (2022). These contributions advance robust projective structure-from-motion methods and biomedical imaging applications.