100 citations · 157 across the 11 of their papers we have counts for
11 papers
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…
Learning to Estimate Two Dense Depths from LiDAR and Event Data
Vincent Brebion, Julien Moreau, Franck Davoine
Event cameras do not produce images, but rather a continuous flow of events, which encode changes of illumination for each pixel independently and asynchronously. While they output…
Decentralized cooperative perception for autonomous vehicles: Learning to value the unknown
Maxime Chaveroche, Franck Davoine, Véronique Cherfaoui
Recently, we have been witnesses of accidents involving autonomous vehicles and their lack of sufficient information. One way to tackle this issue is to benefit from the perception…
Real-Time Optical Flow for Vehicular Perception with Low- and High-Resolution Event Cameras
Vincent Brebion, Julien Moreau, Franck Davoine
Event cameras capture changes of illumination in the observed scene rather than accumulating light to create images. Thus, they allow for applications under high-speed motion and c…
Continuous Conditional Random Field Convolution for Point Cloud Segmentation
Fei Yang, Franck Davoine, Huan Wang +1
Point cloud segmentation is the foundation of 3D environmental perception for modern intelligent systems. To solve this problem and image segmentation, conditional random fields (C…
Efficient Möbius Transformations and their applications to Dempster-Shafer Theory: Clarification and implementation
Maxime Chaveroche, Franck Davoine, Véronique Cherfaoui
Dempster-Shafer Theory (DST) generalizes Bayesian probability theory, offering useful additional information, but suffers from a high computational burden. A lot of work has been d…
Efficient exact computation of the conjunctive and disjunctive decompositions of D-S Theory for information fusion: Translation and extension
Maxime Chaveroche, Franck Davoine, Véronique Cherfaoui
Dempster-Shafer Theory (DST) generalizes Bayesian probability theory, offering useful additional information, but suffers from a high computational burden. A lot of work has been d…
Fusion of neural networks, for LIDAR-based evidential road mapping
Edouard Capellier, Franck Davoine, Veronique Cherfaoui +1
LIDAR sensors are usually used to provide autonomous vehicles with 3D representations of their environment. In ideal conditions, geometrical models could detect the road in LIDAR s…
Focal points and their implications for Möbius Transforms and Dempster-Shafer Theory
Maxime Chaveroche, Franck Davoine, Véronique Cherfaoui
Dempster-Shafer Theory (DST) generalizes Bayesian probability theory, offering useful additional information, but suffers from a much higher computational burden. A lot of work has…
Explicit Inductive Bias for Transfer Learning with Convolutional Networks
Xuhong Li, Yves Grandvalet, Franck Davoine
In inductive transfer learning, fine-tuning pre-trained convolutional networks substantially outperforms training from scratch. When using fine-tuning, the underlying assumption is…