59 citations · 153 across the 29 of their papers we have counts for
16 papers · 1 filter
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…
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…
Uncertainty estimation for Cross-dataset performance in Trajectory prediction
Thomas Gilles, Stefano Sabatini, Dzmitry Tsishkou +2
While a lot of work has been carried on developing trajectory prediction methods, and various datasets have been proposed for benchmarking this task, little study has been done so…
Assessing Cross-dataset Generalization of Pedestrian Crossing Predictors
Joseph Gesnouin, Steve Pechberti, Bogdan Stanciulescu +1
Pedestrian crossing prediction has been a topic of active research, resulting in many new algorithmic solutions. While measuring the overall progress of those solutions over time t…
THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling
Thomas Gilles, Stefano Sabatini, Dzmitry Tsishkou +2
In this paper, we propose THOMAS, a joint multi-agent trajectory prediction framework allowing for an efficient and consistent prediction of multi-agent multi-modal trajectories. W…