3 citations · 8 across the 6 of their papers we have counts for
5 papers · 1 filter
3DLabelProp: Geometric-Driven Domain Generalization for LiDAR Semantic Segmentation in Autonomous Driving
Jules Sanchez, Jean-Emmanuel Deschaud, François Goulette
Domain generalization aims to find ways for deep learning models to maintain their performance despite significant domain shifts between training and inference datasets. This is pa…
COLA: COarse-LAbel multi-source LiDAR semantic segmentation for autonomous driving
Jules Sanchez, Jean-Emmanuel Deschaud, François Goulette
LiDAR semantic segmentation for autonomous driving has been a growing field of interest in recent years. Datasets and methods have appeared and expanded very quickly, but methods h…
ParisLuco3D: A high-quality target dataset for domain generalization of LiDAR perception
Jules Sanchez, Louis Soum-Fontez, Jean-Emmanuel Deschaud +1
LiDAR is an essential sensor for autonomous driving by collecting precise geometric information regarding a scene. %Exploiting this information for perception is interesting as the…
Domain generalization of 3D semantic segmentation in autonomous driving
Jules Sanchez, Jean-Emmanuel Deschaud, Francois Goulette
Using deep learning, 3D autonomous driving semantic segmentation has become a well-studied subject, with methods that can reach very high performance. Nonetheless, because of the l…
COLA: COarse LAbel pre-training for 3D semantic segmentation of sparse LiDAR datasets
Jules Sanchez, Jean-Emmanuel Deschaud, François Goulette
Transfer learning is a proven technique in 2D computer vision to leverage the large amount of data available and achieve high performance with datasets limited in size due to the c…