22 citations · 23 across the 4 of their papers we have counts for
4 papers
Train Till You Drop: Towards Stable and Robust Source-free Unsupervised 3D Domain Adaptation
Björn Michele, Alexandre Boulch, Tuan-Hung Vu +3
We tackle the challenging problem of source-free unsupervised domain adaptation (SFUDA) for 3D semantic segmentation. It amounts to performing domain adaptation on an unlabeled tar…
SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptation
Björn Michele, Alexandre Boulch, Gilles Puy +3
Learning models on one labeled dataset that generalize well on another domain is a difficult task, as several shifts might happen between the data domains. This is notably the case…
ALSO: Automotive Lidar Self-supervision by Occupancy estimation
Alexandre Boulch, Corentin Sautier, Björn Michele +2
We propose a new self-supervised method for pre-training the backbone of deep perception models operating on point clouds. The core idea is to train the model on a pretext task whi…
Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Clouds
Björn Michele, Alexandre Boulch, Gilles Puy +2
While there has been a number of studies on Zero-Shot Learning (ZSL) for 2D images, its application to 3D data is still recent and scarce, with just a few methods limited to classi…