L
Luc Van Gool
Researcher at Katholieke Universiteit Leuven
Publications - 1458
Citations - 137230
Luc Van Gool is an academic researcher from Katholieke Universiteit Leuven. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 133, co-authored 1307 publications receiving 107743 citations. Previous affiliations of Luc Van Gool include Microsoft & ETH Zurich.
Papers
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Proceedings ArticleDOI
SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation
Tao Sun,Mattia Segù,Janis Postels,Yuxuan Wang,Luc Van Gool,Bernt Schiele,Federico Tombari,Fisher Yu +7 more
TL;DR: This paper introduces the largest multi-task synthetic dataset for autonomous driving, SHIFT, which presents discrete and continuous shifts in cloudiness, rain and fog intensity, time of day, and vehicle and pedestrian density.
Markerless computer vision based localization using automatically generated topological maps
TL;DR: This work was motivated by the goal of building a navigation system that could guide people or robots around in large complex urban environments, even in situations in which Global Positioning Systems cannot provide navigational information.
Journal ArticleDOI
MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction
TL;DR: This work proposes a novel Transformer-based method, Multi-stage Spectral-wise Transformer (MST++), for efficient spectral reconstruction that significantly outperforms other state-of-the-art methods.
Journal ArticleDOI
Derivative-Based Scale Invariant Image Feature Detector With Error Resilience
TL;DR: A novel scale-invariant image feature detection algorithm (D-SIFER) using a newly proposed scale-space optimal 10th-order Gaussian derivative (GDO-10) filter, which reaches the jointly optimal Heisenberg's uncertainty of its impulse response in scale and space simultaneously.
Book ChapterDOI
Combining traffic sign detection with 3D tracking towards better driver assistance
TL;DR: A realtime version of driver assistance systems that integrates single view detection with region-based 3D tracking of traffic signs and obtains 3D pose information that is used to establish the relevance of the traffic sign to the driver.