This thesis was realized under the direction of François Goulette and Jean-Emmanuel Deschaud of the Center of Robotics of Mines Paris - PSL.
The defense took place on Tuesday 5 December 2023 at Mines Paris - PSL.
Jury members:
- LANDRIEU Loïc, Chargé de recherche, HDR, École des Ponts ParisTech
- CHECCHIN Paul, Professeur des universités, Université Clermont Auvergne
- CHAINE Raphaëlle, Professeur des universités, Université Claude Bernard Lyon 1, LIRIS
- GOULETTE François, Professeur ENSTA Paris - IP Paris
- DESCHAUD Jean-Emmanuel, Chargé de recherche HDR, Mines Paris - PSL
Summary of the PhD thesis:
LiDAR perception for autonomous vehicles managed to achieve suitable results on the various online benchmarks in the single-domain framework, that is to say when the training domain is the same as the evaluation domain. From there, the fields of research diversified and focused on questions of transferability, robustness and generalization.
This work focuses on generalization issues for LiDAR semantic segmentation. A global overview of the generalization performances, single-source and multi-source, of existing segmentation methods is carried out. To fairly perform these experiments, a dataset, ParisLuco3D, is created specifically to evaluate generalization.
Furthermore, a new single-source domain generalization method, 3DLabelProp, is proposed. This method differs from existing strategies by exploiting the geometry of the data to perform domain alignment rather than learning strategies. Beyond semantic segmentation, this method is also applied to the task of moving object segmentation.
Congratulations to Jules !!!
