67 citations · 194 across the 25 of their papers we have counts for
31 papers
Toward Robust LiDAR Semantic Segmentation for Real-World Deployment: Evaluation under Coarse Labels, Adverse Conditions, and Domain Shifts
Samir Abou Haidar, Alexandre Chariot, Mehdi Darouich +2
LiDAR-based semantic segmentation is a core perception module for autonomous vehicles and mobile robots. Despite the strong performance of recent state-of-the-art methods on standa…
HARP-NeXt: High-Speed and Accurate Range-Point Fusion Network for 3D LiDAR Semantic Segmentation
Samir Abou Haidar, Alexandre Chariot, Mehdi Darouich +2
LiDAR semantic segmentation is crucial for autonomous vehicles and mobile robots, requiring high accuracy and real-time processing, especially on resource-constrained embedded syst…
RayGaussX: Accelerating Gaussian-Based Ray Marching for Real-Time and High-Quality Novel View Synthesis
Hugo Blanc, Jean-Emmanuel Deschaud, Alexis Paljic
RayGauss has achieved state-of-the-art rendering quality for novel-view synthesis on synthetic and indoor scenes by representing radiance and density fields with irregularly distri…
Leg Exoskeleton Odometry using a Limited FOV Depth Sensor
Fabio Elnecave Xavier, Matis Viozelange, Guillaume Burger +3
For leg exoskeletons to operate effectively in real-world environments, they must be able to perceive and understand the terrain around them. However, unlike other legged robots, e…
3DLabelProp: Geometric-Driven Domain Generalization for LiDAR Semantic Segmentation in Autonomous Driving
Jules Sanchez, Jean-Emmanuel Deschaud, François Goulette
Domain generalization aims to find ways for deep learning models to maintain their performance despite significant domain shifts between training and inference datasets. This is pa…
Are We Ready for Real-Time LiDAR Semantic Segmentation in Autonomous Driving?
Samir Abou Haidar, Alexandre Chariot, Mehdi Darouich +2
Within a perception framework for autonomous mobile and robotic systems, semantic analysis of 3D point clouds typically generated by LiDARs is key to numerous applications, such as…
HD-OOD3D: Supervised and Unsupervised Out-of-Distribution object detection in LiDAR data
Louis Soum-Fontez, Jean-Emmanuel Deschaud, François Goulette
Autonomous systems rely on accurate 3D object detection from LiDAR data, yet most detectors are limited to a predefined set of known classes, making them vulnerable to unexpected o…
RayGauss: Volumetric Gaussian-Based Ray Casting for Photorealistic Novel View Synthesis
Hugo Blanc, Jean-Emmanuel Deschaud, Alexis Paljic
Differentiable volumetric rendering-based methods made significant progress in novel view synthesis. On one hand, innovative methods have replaced the Neural Radiance Fields (NeRF)…
COLA: COarse-LAbel multi-source LiDAR semantic segmentation for autonomous driving
Jules Sanchez, Jean-Emmanuel Deschaud, François Goulette
LiDAR semantic segmentation for autonomous driving has been a growing field of interest in recent years. Datasets and methods have appeared and expanded very quickly, but methods h…
ParisLuco3D: A high-quality target dataset for domain generalization of LiDAR perception
Jules Sanchez, Louis Soum-Fontez, Jean-Emmanuel Deschaud +1
LiDAR is an essential sensor for autonomous driving by collecting precise geometric information regarding a scene. %Exploiting this information for perception is interesting as the…
MDT3D: Multi-Dataset Training for LiDAR 3D Object Detection Generalization
Louis Soum-Fontez, Jean-Emmanuel Deschaud, François Goulette
Supervised 3D Object Detection models have been displaying increasingly better performance in single-domain cases where the training data comes from the same environment and sensor…
Multi-IMU Proprioceptive State Estimator for Humanoid Robots
Fabio Elnecave Xavier, Guillaume Burger, Marine Pétriaux +2
Algorithms for state estimation of humanoid robots usually assume that the feet remain flat and in a constant position while in contact with the ground. However, this hypothesis is…
Domain generalization of 3D semantic segmentation in autonomous driving
Jules Sanchez, Jean-Emmanuel Deschaud, Francois Goulette
Using deep learning, 3D autonomous driving semantic segmentation has become a well-studied subject, with methods that can reach very high performance. Nonetheless, because of the l…
Unsigned Distance Field as an Accurate 3D Scene Representation for Neural Scene Completion
Jean Pierre Richa, Jean-Emmanuel Deschaud, François Goulette +1
Scene Completion is the task of completing missing geometry from a partial scan of a scene. Most previous methods compute an implicit representation from range data using a Truncat…
AdaSplats: Adaptive Splatting of Point Clouds for Accurate 3D Modeling and Real-time High-Fidelity LiDAR Simulation
Jean Pierre Richa, Jean-Emmanuel Deschaud, François Goulette +1
LiDAR sensors provide rich 3D information about their surrounding{s} and are becoming increasingly important for autonomous vehicles tasks such as {localization}, semantic segmenta…
COLA: COarse LAbel pre-training for 3D semantic segmentation of sparse LiDAR datasets
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 c…
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…
Riedones3D: a celtic coin dataset for registration and fine-grained clustering
Sofiane Horache, Jean-Emmanuel Deschaud, François Goulette +3
Clustering coins with respect to their die is an important component of numismatic research and crucial for understanding the economic history of tribes (especially when literary p…
CT-ICP: Real-time Elastic LiDAR Odometry with Loop Closure
Pierre Dellenbach, Jean-Emmanuel Deschaud, Bastien Jacquet +1
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…
KITTI-CARLA: a KITTI-like dataset generated by CARLA Simulator
Jean-Emmanuel Deschaud
KITTI-CARLA is a dataset built from the CARLA v0.9.10 simulator using a vehicle with sensors identical to the KITTI dataset. The vehicle thus has a Velodyne HDL64 LiDAR positioned…
3D Point Cloud Registration with Multi-Scale Architecture and Unsupervised Transfer Learning
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. It is based on two components, MS-SVConv and UDGE. Using Mult…
What's in My LiDAR Odometry Toolbox?
Pierre Dellenbach, Jean-Emmanuel Deschaud, Bastien Jacquet +1
With the democratization of 3D LiDAR sensors, precise LiDAR odometries and SLAM are in high demand. New methods regularly appear, proposing solutions ranging from small variations…
Automatic clustering of Celtic coins based on 3D point cloud pattern analysis
Sofiane Horache, Jean-Emmanuel Deschaud, François Goulette +2
The recognition and clustering of coins which have been struck by the same die is of interest for archeological studies. Nowadays, this work can only be performed by experts and is…
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…