19 citations · 20 across the 3 of their papers we have counts for
3 papers
LU-Net: An Efficient Network for 3D LiDAR Point Cloud Semantic Segmentation Based on End-to-End-Learned 3D Features and U-Net
Pierre Biasutti, Vincent Lepetit, Jean-François Aujol +2
We propose LU-Net -- for LiDAR U-Net, a new method for the semantic segmentation of a 3D LiDAR point cloud. Instead of applying some global 3D segmentation method such as PointNet,…
RIU-Net: Embarrassingly simple semantic segmentation of 3D LiDAR point cloud
Pierre Biasutti, Aurélie Bugeau, Jean-François Aujol +1
This paper proposes RIU-Net (for Range-Image U-Net), the adaptation of a popular semantic segmentation network for the semantic segmentation of a 3D LiDAR point cloud. The point cl…
3D Building Model Fitting Using A New Kinetic Framework
Mathieu Brédif, Dider Boldo, Marc Pierrot-Deseilligny +1
We describe a new approach to fit the polyhedron describing a 3D building model to the point cloud of a Digital Elevation Model (DEM). We introduce a new kinetic framework that hid…