67 citations · 72 across the 5 of their papers we have counts for
6 papers
LmPT: Conditional Point Transformer for Anatomical Landmark Detection on 3D Point Clouds
Matteo Bastico, Pierre Onghena, David Ryckelynck +5
Accurate identification of anatomical landmarks is crucial for various medical applications. Traditional manual landmarking is time-consuming and prone to inter-observer variabilit…
MorphoSkel3D: Morphological Skeletonization of 3D Point Clouds for Informed Sampling in Object Classification and Retrieval
Pierre Onghena, Santiago Velasco-Forero, Beatriz Marcotegui
Point clouds are a set of data points in space to represent the 3D geometry of objects. A fundamental step in the processing is to identify a subset of points to represent the shap…
Paris-CARLA-3D: A Real and Synthetic Outdoor Point Cloud Dataset for Challenging Tasks in 3D Mapping
Jean-Emmanuel Deschaud, David Duque, Jean Pierre Richa +3
Paris-CARLA-3D is a dataset of several dense colored point clouds of outdoor environments built by a mobile LiDAR and camera system. The data are composed of two sets with syntheti…
Road Segmentation on low resolution Lidar point clouds for autonomous vehicles
Leonardo Gigli, B Ravi Kiran, Thomas Paul +4
Point cloud datasets for perception tasks in the context of autonomous driving often rely on high resolution 64-layer Light Detection and Ranging (LIDAR) scanners. They are expensi…
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…
Semantic Classification of 3D Point Clouds with Multiscale Spherical Neighborhoods
Hugues Thomas, Jean-Emmanuel Deschaud, Beatriz Marcotegui +2
This paper introduces a new definition of multiscale neighborhoods in 3D point clouds. This definition, based on spherical neighborhoods and proportional subsampling, allows the co…