1 citations · 2 across the 5 of their papers we have counts for
5 papers
OccSim: Multi-kilometer Simulation with Long-horizon Occupancy World Models
Tianran Liu, Shengwen Zhao, Mozhgan Pourkeshavarz +2
Data-driven autonomous driving simulation has long been constrained by its heavy reliance on pre-recorded driving logs or spatial priors, such as HD maps. This fundamental dependen…
AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models
Mozhgan Pourkeshavarz, Tianran Liu, Nicholas Rhinehart
Simulation with realistic traffic agents is essential for validating autonomous driving systems. Existing data-driven simulators learn agent behavior from higher-level abstractions…
Towards foundational LiDAR world models with efficient latent flow matching
Tianran Liu, Shengwen Zhao, Nicholas Rhinehart
LiDAR-based world models offer more structured and geometry-aware representations than their image-based counterparts. However, existing LiDAR world models are narrowly trained; ea…
FSMDet: Vision-guided feature diffusion for fully sparse 3D detector
Tianran Liu, Morteza Mousa Pasandi, Robert Laganiere
Fully sparse 3D detection has attracted an increasing interest in the recent years. However, the sparsity of the features in these frameworks challenges the generation of proposals…
What You See Is What You Detect: Towards better Object Densification in 3D detection
Tianran Liu, Zeping Zhang, Morteza Mousa Pasandi +1
Recent works have demonstrated the importance of object completion in 3D Perception from Lidar signal. Several methods have been proposed in which modules were used to densify the…