1 code implementation • 26 Oct 2023 • Gilles Puy, Spyros Gidaris, Alexandre Boulch, Oriane Siméoni, Corentin Sautier, Patrick Pérez, Andrei Bursuc, Renaud Marlet
In particular, thanks to our scalable distillation method named ScaLR, we show that scaling the 2D and 3D backbones and pretraining on diverse datasets leads to a substantial improvement of the feature quality.
1 code implementation • 26 Oct 2023 • Corentin Sautier, Gilles Puy, Alexandre Boulch, Renaud Marlet, Vincent Lepetit
We present a surprisingly simple and efficient method for self-supervision of 3D backbone on automotive Lidar point clouds.
1 code implementation • CVPR 2023 • Alexandre Boulch, Corentin Sautier, Björn Michele, Gilles Puy, Renaud Marlet
The core idea is to train the model on a pretext task which is the reconstruction of the surface on which the 3D points are sampled, and to use the underlying latent vectors as input to the perception head.
1 code implementation • CVPR 2022 • Corentin Sautier, Gilles Puy, Spyros Gidaris, Alexandre Boulch, Andrei Bursuc, Renaud Marlet
In this context, we propose a self-supervised pre-training method for 3D perception models that is tailored to autonomous driving data.