6 citations · 12 across the 4 of their papers we have counts for
5 papers · 1 filter
Bringing BNNs to Fast Event Processing
Paul Longour, Julien Moreau, Franck Davoine
Binary Neural Networks (BNNs) enable efficient deep learning deployment on resource constrained devices with weights and activations compressed to one bit, substantially reducing m…
DELTA: Dense Depth from Events and LiDAR using Transformer's Attention
Vincent Brebion, Julien Moreau, Franck Davoine
Event cameras and LiDARs provide complementary yet distinct data: respectively, asynchronous detections of changes in lighting versus sparse but accurate depth information at a fix…
Uncertainty-Aware Online Extrinsic Calibration: A Conformal Prediction Approach
Mathieu Cocheteux, Julien Moreau, Franck Davoine
Accurate sensor calibration is crucial for autonomous systems, yet its uncertainty quantification remains underexplored. We present the first approach to integrate uncertainty awar…
MULi-Ev: Maintaining Unperturbed LiDAR-Event Calibration
Mathieu Cocheteux, Julien Moreau, Franck Davoine
Despite the increasing interest in enhancing perception systems for autonomous vehicles, the online calibration between event cameras and LiDAR - two sensors pivotal in capturing c…
PseudoCal: Towards Initialisation-Free Deep Learning-Based Camera-LiDAR Self-Calibration
Mathieu Cocheteux, Julien Moreau, Franck Davoine
Camera-LiDAR extrinsic calibration is a critical task for multi-sensor fusion in autonomous systems, such as self-driving vehicles and mobile robots. Traditional techniques often r…