Zobrazeno 1 - 10
of 95
pro vyhledávání: '"Goulette, Francois"'
LiDAR semantic segmentation for autonomous driving has been a growing field of interest in the past few years. Datasets and methods have appeared and expanded very quickly, but methods have not been updated to exploit this new availability of data an
Externí odkaz:
http://arxiv.org/abs/2311.03017
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 amount of available data increases. As the performance of various LiD
Externí odkaz:
http://arxiv.org/abs/2310.16542
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 as the testing data. However, in real-world scenarios data from the t
Externí odkaz:
http://arxiv.org/abs/2308.01000
Autor:
Xavier, Fabio Elnecave, Burger, Guillaume, Pétriaux, Marine, Deschaud, Jean-Emmanuel, Goulette, François
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 easily violated while walking, especially for human-like gaits with h
Externí odkaz:
http://arxiv.org/abs/2307.14125
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 limited size of the training datasets, these models cannot see every ty
Externí odkaz:
http://arxiv.org/abs/2212.04245
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 Truncated Signed Distance Function (T-SDF) computed on a 3D grid as input to
Externí odkaz:
http://arxiv.org/abs/2203.09167
LiDAR sensors provide rich 3D information about their surrounding{s} and are becoming increasingly important for autonomous vehicles tasks such as {localization}, semantic segmentation, object detection, and tracking. {Simulation} accelerates the tes
Externí odkaz:
http://arxiv.org/abs/2203.09155
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. In 3D, annotation is known to be a c
Externí odkaz:
http://arxiv.org/abs/2202.06884
Autor:
Horache, Sofiane, Deschaud, Jean-Emmanuel, Goulette, François, Gruel, Katherine, Lejars, Thierry, Masson, Olivier
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 production does not exist, in celtic culture). It is a very hard task t
Externí odkaz:
http://arxiv.org/abs/2109.15033
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. For this, we propose a new real-time LiDAR-onl
Externí odkaz:
http://arxiv.org/abs/2109.12979