48 citations · 58 across the 9 of their papers we have counts for
14 papers
DynEoMT: Learning Object Dynamicity from Online Segmentation Queries
Calvin Galagain, Martyna Poreba, François Goulette
Video segmentation models recognize and track objects over time, but they do not indicate whether each segmented region moves independently of the observing camera. This dynamicity…
Towards 3D karst underwater scene reconstruction from rotating sonar data
Georgios Evangelos Margaritis, Lionel Lapierre, Simon Rohou +3
Karst aquifers provide critical freshwater resources but pose significant hazards due to their complex and poorly understood subsurface geometry. Mapping these environments is chal…
Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey
Calvin Galagain, Martyna Poreba, François Goulette
Semantic SLAM holds promise for robust robot navigation in complex environments, but its practicality on embedded systems remains uncertain. This paper presents a comparative surve…
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…
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…
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…
Computational Fluid Dynamics on 3D Point Set Surfaces
Hassan Bouchiba, Simon Santoso, Jean-Emmanuel Deschaud +3
Computational fluid dynamics (CFD) in many cases requires designing 3D models manually, which is a tedious task that requires specific skills. In this paper, we present a novel met…
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…
Paris-Lille-3D: a large and high-quality ground truth urban point cloud dataset for automatic segmentation and classification
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). We describe how the dataset is obtained…
Invariant EKF Design for Scan Matching-aided Localization
Martin Barczyk, Silvère Bonnabel, Jean-Emmanuel Deschaud +1
Localization in indoor environments is a technique which estimates the robot's pose by fusing data from onboard motion sensors with readings of the environment, in our case obtaine…
On the Covariance of ICP-based Scan-matching Techniques
Silvère Bonnabel, Martin Barczyk, François Goulette
This paper considers the problem of estimating the covariance of roto-translations computed by the Iterative Closest Point (ICP) algorithm. The problem is relevant for localization…
Experimental Implementation of an Invariant Extended Kalman Filter-based Scan Matching SLAM
Martin Barczyk, Silvère Bonnabel, Jean-Emmanuel Deschaud +1
We describe an application of the Invariant Extended Kalman Filter (IEKF) design methodology to the scan matching SLAM problem. We review the theoretical foundations of the IEKF an…
Accurate 3D maps from depth images and motion sensors via nonlinear Kalman filtering
Thibault Hervier, Silvère Bonnabel, François Goulette
This paper investigates the use of depth images as localisation sensors for 3D map building. The localisation information is derived from the 3D data thanks to the ICP (Iterative C…