28 citations · 71 across the 18 of their papers we have counts for
5 papers · 1 filter
Parameter-free structure-texture image decomposition by unrolling
Laura Girometti, Jean-François Aujol, Antoine Guennec +1
In this work, we propose a parameter-free and efficient method to tackle the structure-texture image decomposition problem. In particular, we present a neural network LPR-NET based…
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…
Variational Methods for Normal Integration
Yvain Quéau, Jean-Denis Durou, Jean-François Aujol
The need for an efficient method of integration of a dense normal field is inspired by several computer vision tasks, such as shape-from-shading, photometric stereo, deflectometry,…
Normal Integration: A Survey
Yvain Quéau, Jean-Denis Durou, Jean-François Aujol
The need for efficient normal integration methods is driven by several computer vision tasks such as shape-from-shading, photometric stereo, deflectometry, etc. In the first part o…