59 citations · 153 across the 29 of their papers we have counts for
31 papers
DOC-Depth: A novel approach for dense depth ground truth generation
Simon de Moreau, Mathias Corsia, Hassan Bouchiba +4
Accurate depth information is essential for many computer vision applications. Yet, no available dataset recording method allows for fully dense accurate depth estimation in a larg…
LED: Light Enhanced Depth Estimation at Night
Simon de Moreau, Yasser Almehio, Andrei Bursuc +3
Nighttime camera-based depth estimation is a highly challenging task, especially for autonomous driving applications, where accurate depth perception is essential for ensuring safe…
Mesoscale Traffic Forecasting for Real-Time Bottleneck and Shockwave Prediction
Raphael Chekroun, Han Wang, Jonathan Lee +4
Accurate real-time traffic state forecasting plays a pivotal role in traffic control research. In particular, the CIRCLES consortium project necessitates predictive techniques to m…
MBAPPE: MCTS-Built-Around Prediction for Planning Explicitly
Raphael Chekroun, Thomas Gilles, Marin Toromanoff +2
We present MBAPPE, a novel approach to motion planning for autonomous driving combining tree search with a partially-learned model of the environment. Leveraging the inherent expla…
TSGN: Temporal Scene Graph Neural Networks with Projected Vectorized Representation for Multi-Agent Motion Prediction
Yunong Wu, Thomas Gilles, Bogdan Stanciulescu +1
Predicting future motions of nearby agents is essential for an autonomous vehicle to take safe and effective actions. In this paper, we propose TSGN, a framework using Temporal Sce…
The Audio-Visual BatVision Dataset for Research on Sight and Sound
Amandine Brunetto, Sascha Hornauer, Stella X. Yu +1
Vision research showed remarkable success in understanding our world, propelled by datasets of images and videos. Sensor data from radar, LiDAR and cameras supports research in rob…