67 citations · 194 across the 25 of their papers we have counts for
19 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…
Semantic Classification of 3D Point Clouds with Multiscale Spherical Neighborhoods
Hugues Thomas, Jean-Emmanuel Deschaud, Beatriz Marcotegui +2
This paper introduces a new definition of multiscale neighborhoods in 3D point clouds. This definition, based on spherical neighborhoods and proportional subsampling, allows the co…
Classification of Point Cloud Scenes with Multiscale Voxel Deep Network
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. Solutions are given to the problems encountered working…