34 citations · 36 across the 2 of their papers we have counts for
10 papers
Riedones3D: a celtic coin dataset for registration and fine-grained clustering
Sofiane Horache, Jean-Emmanuel Deschaud, François Goulette +3
Clustering coins with respect to their die is an important component of numismatic research and crucial for understanding the economic history of tribes (especially when literary p…
KITTI-CARLA: a KITTI-like dataset generated by CARLA Simulator
Jean-Emmanuel Deschaud
KITTI-CARLA is a dataset built from the CARLA v0.9.10 simulator using a vehicle with sensors identical to the KITTI dataset. The vehicle thus has a Velodyne HDL64 LiDAR positioned…
3D Point Cloud Registration with Multi-Scale Architecture and Unsupervised Transfer Learning
Sofiane Horache, Jean-Emmanuel Deschaud, François Goulette
We propose a method for generalizing deep learning for 3D point cloud registration on new, totally different datasets. It is based on two components, MS-SVConv and UDGE. Using Mult…
What's in My LiDAR Odometry Toolbox?
Pierre Dellenbach, Jean-Emmanuel Deschaud, Bastien Jacquet +1
With the democratization of 3D LiDAR sensors, precise LiDAR odometries and SLAM are in high demand. New methods regularly appear, proposing solutions ranging from small variations…
Automatic clustering of Celtic coins based on 3D point cloud pattern analysis
Sofiane Horache, Jean-Emmanuel Deschaud, François Goulette +2
The recognition and clustering of coins which have been struck by the same die is of interest for archeological studies. Nowadays, this work can only be performed by experts and is…
KPConv: Flexible and Deformable Convolution for Point Clouds
Hugues Thomas, Charles R. Qi, Jean-Emmanuel Deschaud +3
We present Kernel Point Convolution (KPConv), a new design of point convolution, i.e. that operates on point clouds without any intermediate representation. The convolution weights…