37 citations · 137 across the 14 of their papers we have counts for
3 papers · 1 filter
Reconstructing Objects in-the-wild for Realistic Sensor Simulation
Ze Yang, Sivabalan Manivasagam, Yun Chen +3
Reconstructing objects from real world data and rendering them at novel views is critical to bringing realism, diversity and scale to simulation for robotics training and testing.…
Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion
Lunjun Zhang, Yuwen Xiong, Ze Yang +3
Learning world models can teach an agent how the world works in an unsupervised manner. Even though it can be viewed as a special case of sequence modeling, progress for scaling wo…
Rethinking Closed-loop Training for Autonomous Driving
Chris Zhang, Runsheng Guo, Wenyuan Zeng +5
Recent advances in high-fidelity simulators have enabled closed-loop training of autonomous driving agents, potentially solving the distribution shift in training v.s. deployment a…