129 citations · 192 across the 22 of their papers we have counts for
15 papers · 1 filter
AssemblyNet: A large ensemble of CNNs for 3D Whole Brain MRI Segmentation
Pierrick Coupé, Boris Mansencal, Michaël Clément +5
Whole brain segmentation using deep learning (DL) is a very challenging task since the number of anatomical labels is very high compared to the number of available training images.…
Smart Hypothesis Generation for Efficient and Robust Room Layout Estimation
Martin Hirzer, Peter M. Roth, Vincent Lepetit
We propose a novel method to efficiently estimate the spatial layout of a room from a single monocular RGB image. As existing approaches based on low-level feature extraction, foll…
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,…
CorNet: Generic 3D Corners for 6D Pose Estimation of New Objects without Retraining
Giorgia Pitteri, Slobodan Ilic, Vincent Lepetit
We present a novel approach to the detection and 3D pose estimation of objects in color images. Its main contribution is that it does not require any training phases nor data for n…
Sparse-to-Dense Hypercolumn Matching for Long-Term Visual Localization
Hugo Germain, Guillaume Bourmaud, Vincent Lepetit
We propose a novel approach to feature point matching, suitable for robust and accurate outdoor visual localization in long-term scenarios. Given a query image, we first match it a…
On Object Symmetries and 6D Pose Estimation from Images
Giorgia Pitteri, Michaël Ramamonjisoa, Slobodan Ilic +1
Objects with symmetries are common in our daily life and in industrial contexts, but are often ignored in the recent literature on 6D pose estimation from images. In this paper, we…