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
More filters
Proceedings Article
Flow-Guided Sparse Transformer for Video Deblurring
Jing Lin,Xiaowan Hu,Haoqian Wang,Youliang Yan,Xueyi Zou,Henghui Ding,Yulun Zhang,Radu Timofte,Luc Van Gool +8 more
TL;DR: This paper proposes a novel framework, Flow-Guided Sparse Transformer (FGST), for video deblurring, which outperforms state-of-the-art (SOTA) methods on both DVD and GOPRO datasets and yields visually pleasant results in real videodeblurring.
Book ChapterDOI
Destination flow for crowd simulation
TL;DR: A crowd simulation that captures some of the semantics of a specific scene by partly reproducing its motion behaviors, both at a lower level using a steering model and at the higher level of goal selection, and which can easily integrate real and virtual agents in a mixed reality simulation.
Proceedings ArticleDOI
Mobile phone and cloud — A dream team for 3D reconstruction
TL;DR: This paper presents an implementation of a combination of a regular mobile phone as frontend with a centralized server plus annex cloud as backend for collaborative, on-line 3D reconstruction and demonstrates its advantages via real-life reconstructions.
Proceedings ArticleDOI
An Integer Linear Programming Model for View Selection on Overlapping Camera Clusters
TL;DR: A novel formulation for view selection is proposed, where cameras are modeled with binary variables, while the linear constraints enforce the completeness of the 3D reconstruction, and the solution of the ILP leads to an optimal subset of selected cameras.
Book ChapterDOI
Nested sparse quantization for efficient feature coding
TL;DR: A novel assignment-based encoding formulation is presented that allows for the fusion of assignment- based encoding and sparse coding into one formulation, and is able to encode one million images using 4 CPUs in a single day, while maintaining a good performance.