5 papers
PPT: Pretraining with Pseudo-Labeled Trajectories for Motion Forecasting
Yihong Xu, Yuan Yin, Ãloi Zablocki +3
Accurately predicting how agents move in dynamic scenes is essential for safe autonomous driving. State-of-the-art motion forecasting models rely on datasets with manually annotate…
Is clustering enough for LiDAR instance segmentation? A state-of-the-art training-free baseline
Corentin Sautier, Gilles Puy, Alexandre Boulch +2
Panoptic segmentation of LiDAR point clouds is fundamental to outdoor scene understanding, with autonomous driving being a primary application. While state-of-the-art approaches ty…
LiDPM: Rethinking Point Diffusion for Lidar Scene Completion
Tetiana Martyniuk, Gilles Puy, Alexandre Boulch +2
Training diffusion models that work directly on lidar points at the scale of outdoor scenes is challenging due to the difficulty of generating fine-grained details from white noise…
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…
VaViM and VaVAM: Autonomous Driving through Video Generative Modeling
Florent Bartoccioni, Elias Ramzi, Victor Besnier +14
We explore the potential of large-scale generative video models for autonomous driving, introducing an open-source auto-regressive video model (VaViM) and its companion video-actio…