2 citations · 2 across the 3 of their papers we have counts for
3 papers
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…
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…