Spacetime (4D) occupancy forecasting explicitly models the future development of self-
driving vehicles (SDVs) themselves and their surrounding environment, which is highly …

Labeling LiDAR point clouds is notoriously time-and-energy-consuming, which spurs recent
unsupervised 3D representation learning methods to alleviate the labeling burden in LiDAR …

LiDAR scene generation has been developing rapidly recently. However, existing methods
primarily focus on generating static and single-frame scenes, overlooking the inherently …

This paper targets the challenge of real-time LiDAR re-simulation in dynamic driving
scenarios. Recent approaches utilize neural radiance fields combined with the physical …

Although recent efforts have extended Neural Radiance Fields (NeRF) into LiDAR point
cloud synthesis, the majority of existing works exhibit a strong dependence on precomputed …

Ensuring the safety of autonomous robots, such as self-driving vehicles, requires extensive
testing across diverse driving scenarios. Simulation is a key ingredient for conducting such …

We propose a framework to edit real-world Lidar scans with novel object layouts while
preserving a realistic background environment. Compared to the synthetic data generation …

LiDAR simulation plays a crucial role in closed-loop simulation for autonomous driving.
Although recent advancements, such as the use of reconstructed mesh and Neural …

With the evolution of data-driven autonomous driving technology, transferring driving
responsibility from humans to machines is now feasible. Addressing the long-tail distribution …

LiDAR scene generation has been developing rapidly recently. However, existing methods
primarily focus on generating static and single-frame scenes, overlooking the inherently …