| EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage use case N Díaz-Rodríguez, A Lamas, J Sanchez, G Franchi, I Donadello, S Tabik, ... Information Fusion 79, 58-83, 2022 | 105 | 2022 |
| Domain generalization of 3d semantic segmentation in autonomous driving J Sanchez, JE Deschaud, F Goulette Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 31 | 2023 |
| COLA: COarse LAbel pre-training for 3D semantic segmentation of sparse LiDAR datasets J Sanchez, JE Deschaud, F Goulette 2023 IEEE International Conference on Robotics and Automation (ICRA), 11343 …, 2023 | 7 | 2023 |
| Cola: Coarse-label multi-source lidar semantic segmentation for autonomous driving J Sanchez, JE Deschaud, F Goulette arXiv preprint arXiv:2311.03017, 2023 | 3 | 2023 |
| ParisLuco3D: A high-quality target dataset for domain generalization of LiDAR perception J Sanchez, L Soum-Fontez, JE Deschaud, F Goulette IEEE Robotics and Automation Letters, 2024 | 2 | 2024 |
| 3DLabelProp: Geometric-Driven Domain Generalization for LiDAR Semantic Segmentation in Autonomous Driving J Sanchez, JE Deschaud, F Goulette arXiv preprint arXiv:2501.14605, 2025 | | 2025 |
| Généralisation de domaine pour la segmentation sémantique de données LiDAR pour le véhicule autonome J Sanchez Université Paris sciences et lettres, 2023 | | 2023 |
Jean-Emmanuel DeschaudAssociate Professor in Robotics and 3D Vision, Center for Robotics, Mines ParisTech, PSL UniversityCorreu electrònic verificat a mines-paristech.fr