15 citations · 57 across the 12 of their papers we have counts for
5 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.…
UltraLiDAR: Learning Compact Representations for LiDAR Completion and Generation
Yuwen Xiong, Wei-Chiu Ma, Jingkang Wang +1
LiDAR provides accurate geometric measurements of the 3D world. Unfortunately, dense LiDARs are very expensive and the point clouds captured by low-beam LiDAR are often sparse. To…
CADSim: Robust and Scalable in-the-wild 3D Reconstruction for Controllable Sensor Simulation
Jingkang Wang, Sivabalan Manivasagam, Yun Chen +5
Realistic simulation is key to enabling safe and scalable development of % self-driving vehicles. A core component is simulating the sensors so that the entire autonomy system can…
UniSim: A Neural Closed-Loop Sensor Simulator
Ze Yang, Yun Chen, Jingkang Wang +4
Rigorously testing autonomy systems is essential for making safe self-driving vehicles (SDV) a reality. It requires one to generate safety critical scenarios beyond what can be col…
Learning to Communicate and Correct Pose Errors
Nicholas Vadivelu, Mengye Ren, James Tu +2
Learned communication makes multi-agent systems more effective by aggregating distributed information. However, it also exposes individual agents to the threat of erroneous message…