no code implementations • 6 Nov 2023 • Jules Sanchez, Jean-Emmanuel Deschaud, François Goulette
LiDAR semantic segmentation for autonomous driving has been a growing field of interest in the past few years.
no code implementations • 25 Oct 2023 • Jules Sanchez, Louis Soum-Fontez, Jean-Emmanuel Deschaud, Francois Goulette
LiDAR is an essential sensor for autonomous driving by collecting precise geometric information regarding a scene.
no code implementations • 2 Aug 2023 • Louis Soum-Fontez, Jean-Emmanuel Deschaud, François Goulette
Therefore, we decided to leverage the information available from several annotated source datasets with our Multi-Dataset Training for 3D Object Detection (MDT3D) method to increase the robustness of 3D object detection models when tested in a new environment with a different sensor configuration.
1 code implementation • ICCV 2023 • Jules Sanchez, Jean-Emmanuel Deschaud, Francois Goulette
This method relies on leveraging the geometry and sequentiality of the LiDAR data to enhance its generalization performances by working on partially accumulated point clouds.
no code implementations • 17 Mar 2022 • Jean Pierre Richa, Jean-Emmanuel Deschaud, François Goulette, Nicolas Dalmasso
The proposed UDF is simple, and efficient as a geometry representation, and can be computed on any point cloud.
1 code implementation • 14 Feb 2022 • Jules Sanchez, Jean-Emmanuel Deschaud, François Goulette
Transfer learning is a proven technique in 2D computer vision to leverage the large amount of data available and achieve high performance with datasets limited in size due to the cost of acquisition or annotation.
no code implementations • 22 Nov 2021 • Jean-Emmanuel Deschaud, David Duque, Jean Pierre Richa, Santiago Velasco-Forero, Beatriz Marcotegui, and François Goulette
The data are composed of two sets with synthetic data from the open source CARLA simulator (700 million points) and real data acquired in the city of Paris (60 million points), hence the name Paris-CARLA-3D.
no code implementations • 30 Sep 2021 • Sofiane Horache, Jean-Emmanuel Deschaud, François Goulette, Katherine Gruel, Thierry Lejars, Olivier Masson
With this dataset, we propose two benchmarks, one for point cloud registration, essential for coin die recognition, and a benchmark of coin die clustering.
1 code implementation • 27 Sep 2021 • Pierre Dellenbach, Jean-Emmanuel Deschaud, Bastien Jacquet, François Goulette
Multi-beam LiDAR sensors are increasingly used in robotics, particularly with autonomous cars for localization and perception tasks, both relying on the ability to build a precise map of the environment.
1 code implementation • 17 Aug 2021 • Jean-Emmanuel Deschaud
This dataset thus makes it possible to improve transfer learning methods from a synthetic dataset to a real dataset.
1 code implementation • 26 Mar 2021 • 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.
Ranked #2 on Point Cloud Registration on 3DMatch Benchmark
1 code implementation • 17 Mar 2021 • Pierre Dellenbach, Jean-Emmanuel Deschaud, Bastien Jacquet, François Goulette
With the democratization of 3D LiDAR sensors, precise LiDAR odometries and SLAM are in high demand.
no code implementations • 12 May 2020 • Sofiane Horache, Jean-Emmanuel Deschaud, François Goulette, Katherine Gruel, Thierry Lejars
The recognition and clustering of coins which have been struck by the same die is of interest for archeological studies.
9 code implementations • ICCV 2019 • Hugues Thomas, Charles R. Qi, Jean-Emmanuel Deschaud, Beatriz Marcotegui, François Goulette, Leonidas J. Guibas
Furthermore, these locations are continuous in space and can be learned by the network.
Ranked #1 on 3D Semantic Segmentation on DALES
no code implementations • 1 Aug 2018 • Hugues Thomas, Jean-Emmanuel Deschaud, Beatriz Marcotegui, François Goulette, Yann Le Gall
This paper introduces a new definition of multiscale neighborhoods in 3D point clouds.
Ranked #12 on Semantic Segmentation on Semantic3D
1 code implementation • 10 Apr 2018 • Xavier Roynard, Jean-Emmanuel Deschaud, François Goulette
In this article we describe a new convolutional neural network (CNN) to classify 3D point clouds of urban or indoor scenes.
Ranked #11 on Semantic Segmentation on Semantic3D
no code implementations • 23 Feb 2018 • Jean-Emmanuel Deschaud
The Simultaneous Localization And Mapping (SLAM) problem has been well studied in the robotics community, especially using mono, stereo cameras or depth sensors.
Robotics
no code implementations • 30 Nov 2017 • Xavier Roynard, Jean-Emmanuel Deschaud, François Goulette
This paper introduces a new Urban Point Cloud Dataset for Automatic Segmentation and Classification acquired by Mobile Laser Scanning (MLS).
Ranked #7 on LIDAR Semantic Segmentation on Paris-Lille-3D
no code implementations • 3 Dec 2014 • Jean-Emmanuel Deschaud, Xavier Brun, François Goulette
We present here a real time mobile mapping system mounted on a vehicle.