Leuven | More than two weeks ago
The aim of this thesis is to investigate existing radar-camera completion approaches. In addition, we will investigate a self-learning fusion approach for scene reconstruction in adverse surrounding conditions.
Prof. Hichem Sahli (IMEC / VUB) <email@example.com)
André Bourdoux (IMEC) <Andre.Bourdoux@imec.be>
Seyed Hamed Javadi (IMEC) <firstname.lastname@example.org>
 S.F. Bhat, I. Alhashim, and P. Wonka. “AdaBins: Depth estimation using adaptive bins”. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4009–4018, 2021.
 M.A.U. Khan, D. Nazir, A. Pagani, H. Mokayed, M. Liwicki, D. Stricker, M.Z. A Afzal, “Comprehensive Survey of Depth Completion Approaches”, Sensors 2022, 22, 6969.
 C. Fu, C. Mertz and J. M. Dolan, "LIDAR and Monocular Camera Fusion: On-road Depth Completion for Autonomous Driving," 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand, 2019, pp. 273-278, S. Gasperini, P. Koch, V. Dallabetta, N. Navab, B. Busam and F. Tombari, "R4Dyn: Exploring Radar for Self-Supervised Monocular Depth Estimation of Dynamic Scenes," 2021 International Conference on 3D Vision (3DV), London, United Kingdom, 2021, pp. 751-760.
Type of project: Combination of internship and thesis, Thesis
Required degree: Master of Engineering Science, Master of Science, Master of Engineering Technology
Required background: Electrotechnics/Electrical Engineering, Computer Science, Physics
Supervising scientist(s): For further information or for application, please contact: Hichem Sahli (Hichem.Sahli@imec.be) and Andre Bourdoux (Andre.Bourdoux@imec.be) and Seyed Hamed Javadi (email@example.com)