841 citations · 7.7k across the 158 of their papers we have counts for
15 papers · 1 filter
LightSim: Neural Lighting Simulation for Urban Scenes
Ava Pun, Gary Sun, Jingkang Wang +5
Different outdoor illumination conditions drastically alter the appearance of urban scenes, and they can harm the performance of image-based robot perception systems if not seen du…
4D-Former: Multimodal 4D Panoptic Segmentation
Ali Athar, Enxu Li, Sergio Casas +1
4D panoptic segmentation is a challenging but practically useful task that requires every point in a LiDAR point-cloud sequence to be assigned a semantic class label, and individua…
Real-Time Neural Rasterization for Large Scenes
Jeffrey Yunfan Liu, Yun Chen, Ze Yang +3
We propose a new method for realistic real-time novel-view synthesis (NVS) of large scenes. Existing neural rendering methods generate realistic results, but primarily work for sma…
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.…
Towards Unsupervised Object Detection From LiDAR Point Clouds
Lunjun Zhang, Anqi Joyce Yang, Yuwen Xiong +4
In this paper, we study the problem of unsupervised object detection from 3D point clouds in self-driving scenes. We present a simple yet effective method that exploits (i) point c…
MemorySeg: Online LiDAR Semantic Segmentation with a Latent Memory
Enxu Li, Sergio Casas, Raquel Urtasun
Semantic segmentation of LiDAR point clouds has been widely studied in recent years, with most existing methods focusing on tackling this task using a single scan of the environmen…
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…
Adv3D: Generating Safety-Critical 3D Objects through Closed-Loop Simulation
Jay Sarva, Jingkang Wang, James Tu +3
Self-driving vehicles (SDVs) must be rigorously tested on a wide range of scenarios to ensure safe deployment. The industry typically relies on closed-loop simulation to evaluate h…
LabelFormer: Object Trajectory Refinement for Offboard Perception from LiDAR Point Clouds
Anqi Joyce Yang, Sergio Casas, Nikita Dvornik +5
A major bottleneck to scaling-up training of self-driving perception systems are the human annotations required for supervision. A promising alternative is to leverage "auto-labell…
Learning Realistic Traffic Agents in Closed-loop
Chris Zhang, James Tu, Lunjun Zhang +3
Realistic traffic simulation is crucial for developing self-driving software in a safe and scalable manner prior to real-world deployment. Typically, imitation learning (IL) is use…
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…
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…
Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving
Ben Agro, Quinlan Sykora, Sergio Casas +1
A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future behavior of other traffic participants. Existing works either perform object detection…
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…