72 citations · 285 across the 34 of their papers we have counts for
5 papers · 1 filter
BEVContrast: Self-Supervision in BEV Space for Automotive Lidar Point Clouds
Corentin Sautier, Gilles Puy, Alexandre Boulch +2
We present a surprisingly simple and efficient method for self-supervision of 3D backbone on automotive Lidar point clouds. We design a contrastive loss between features of Lidar s…
Three Pillars improving Vision Foundation Model Distillation for Lidar
Gilles Puy, Spyros Gidaris, Alexandre Boulch +5
Self-supervised image backbones can be used to address complex 2D tasks (e.g., semantic segmentation, object discovery) very efficiently and with little or no downstream supervisio…
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…
RangeViT: Towards Vision Transformers for 3D Semantic Segmentation in Autonomous Driving
Angelika Ando, Spyros Gidaris, Andrei Bursuc +3
Casting semantic segmentation of outdoor LiDAR point clouds as a 2D problem, e.g., via range projection, is an effective and popular approach. These projection-based methods usuall…
Using a Waffle Iron for Automotive Point Cloud Semantic Segmentation
Gilles Puy, Alexandre Boulch, Renaud Marlet
Semantic segmentation of point clouds in autonomous driving datasets requires techniques that can process large numbers of points efficiently. Sparse 3D convolutions have become th…