7 papers
U4D: Uncertainty-Aware 4D World Modeling from LiDAR Sequences
Xiang Xu, Alan Liang, Youquan Liu +4
Modeling dynamic 3D environments from LiDAR sequences is central to building reliable 4D worlds for autonomous driving and embodied AI. Existing generative frameworks, however, oft…
3EED: Ground Everything Everywhere in 3D
Rong Li, Yuhao Dong, Tianshuai Hu +7
Visual grounding in 3D is the key for embodied agents to localize language-referred objects in open-world environments. However, existing benchmarks are limited to indoor focus, si…
Learning to Generate 4D LiDAR Sequences
Ao Liang, Youquan Liu, Yu Yang +5
While generative world models have advanced video and occupancy-based data synthesis, LiDAR generation remains underexplored despite its importance for accurate 3D perception. Exte…
La La LiDAR: Large-Scale Layout Generation from LiDAR Data
Youquan Liu, Lingdong Kong, Weidong Yang +5
Controllable generation of realistic LiDAR scenes is crucial for applications such as autonomous driving and robotics. While recent diffusion-based models achieve high-fidelity LiD…
Veila: Panoramic LiDAR Generation from a Monocular RGB Image
Youquan Liu, Lingdong Kong, Weidong Yang +8
Realistic and controllable panoramic LiDAR data generation is critical for scalable 3D perception in autonomous driving and robotics. Existing methods either perform unconditional…
LiDARCrafter: Dynamic 4D World Modeling from LiDAR Sequences
Ao Liang, Youquan Liu, Yu Yang +5
Generative world models have become essential data engines for autonomous driving, yet most existing efforts focus on videos or occupancy grids, overlooking the unique LiDAR proper…