2 citations · 5 across the 9 of their papers we have counts for
10 papers
GaussRender: Learning 3D Occupancy with Gaussian Rendering
Loïck Chambon, Eloi Zablocki, Alexandre Boulch +2
Understanding the 3D geometry and semantics of driving scenes is critical for safe autonomous driving. Recent advances in 3D occupancy prediction have improved scene representation…
Annealed Winner-Takes-All for Motion Forecasting
Yihong Xu, Victor Letzelter, Mickaël Chen +2
In autonomous driving, motion prediction aims at forecasting the future trajectories of nearby agents, helping the ego vehicle to anticipate behaviors and drive safely. A key chall…
Annealed Multiple Choice Learning: Overcoming limitations of Winner-takes-all with annealing
David Perera, Victor Letzelter, Théo Mariotte +4
We introduce Annealed Multiple Choice Learning (aMCL) which combines simulated annealing with MCL. MCL is a learning framework handling ambiguous tasks by predicting a small set of…
Valeo4Cast: A Modular Approach to End-to-End Forecasting
Yihong Xu, Éloi Zablocki, Alexandre Boulch +11
Motion forecasting is crucial in autonomous driving systems to anticipate the future trajectories of surrounding agents such as pedestrians, vehicles, and traffic signals. In end-t…
Manipulating Trajectory Prediction with Backdoors
Kaouther Messaoud, Kathrin Grosse, Mickael Chen +3
Autonomous vehicles ought to predict the surrounding agents' trajectories to allow safe maneuvers in uncertain and complex traffic situations. As companies increasingly apply traje…
Reliability in Semantic Segmentation: Can We Use Synthetic Data?
Thibaut Loiseau, Tuan-Hung Vu, Mickael Chen +2
Assessing the robustness of perception models to covariate shifts and their ability to detect out-of-distribution (OOD) inputs is crucial for safety-critical applications such as a…
PointBeV: A Sparse Approach to BeV Predictions
Loick Chambon, Eloi Zablocki, Mickael Chen +3
Bird's-eye View (BeV) representations have emerged as the de-facto shared space in driving applications, offering a unified space for sensor data fusion and supporting various down…
Resilient Multiple Choice Learning: A learned scoring scheme with application to audio scene analysis
Victor Letzelter, Mathieu Fontaine, Mickaël Chen +3
We introduce Resilient Multiple Choice Learning (rMCL), an extension of the MCL approach for conditional distribution estimation in regression settings where multiple targets may b…
DiffHPE: Robust, Coherent 3D Human Pose Lifting with Diffusion
Cédric Rommel, Eduardo Valle, Mickaël Chen +4
We present an innovative approach to 3D Human Pose Estimation (3D-HPE) by integrating cutting-edge diffusion models, which have revolutionized diverse fields, but are relatively un…
Towards Motion Forecasting with Real-World Perception Inputs: Are End-to-End Approaches Competitive?
Yihong Xu, Loïck Chambon, Éloi Zablocki +4
Motion forecasting is crucial in enabling autonomous vehicles to anticipate the future trajectories of surrounding agents. To do so, it requires solving mapping, detection, trackin…