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20212025
most citedDomain generalization of 3D semantic segmentation in autonomous driving

3 citations · 8 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2025

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…

cs.CV2023★ 1 cited

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…

cs.CV2023★ 2 cited

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…

cs.CV2022★ 3 cited

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…

cs.CV2022

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…

cs.LG2021★ 2 cited

EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage use case

Natalia Díaz-Rodríguez, Alberto Lamas, Jules Sanchez +7

The latest Deep Learning (DL) models for detection and classification have achieved an unprecedented performance over classical machine learning algorithms. However, DL models are…