841 citations · 7.7k across the 158 of their papers we have counts for
7 papers · 1 filter
TriO: Tri-Modal Unsupervised Occupancy World Model for Anything Perception
Quinlan Sykora, Sourav Biswas, Christopher Diehl +3
We present TriO, a multi-modal unsupervised world model that predicts 4D occupancy, obstacle segmentation, flow and LiDAR. In contrast to prior work, TriO utilizes three distinct s…
Diffusion-guided Generalizable Enhancer for Urban Scene Reconstruction
Henry Che, Jingkang Wang, Yun Chen +3
Urban scene reconstruction from real-world observations has emerged as a powerful tool for self-driving development and testing. While current neural rendering approaches achieve h…
Traffic Scenario Orchestration from Language via Constraint Satisfaction
Frieda Rong, Chris Zhang, Kelvin Wong +1
Autonomous vehicles (AVs) require extensive testing in simulation, but test case generation for driving scenarios is laborious. The desired scenarios are often out-of-distribution…
Conditional Flow-VAE for Safety-Critical Traffic Scenario Generation
Zimu Gong, Brian Zhaoning Zhang, Chris Zhang +2
Safety-critical scenarios are essential for the development of autonomous vehicles (AVs) but are rare in real-world driving data. While simulation offers a way to generate such sce…
GenAssets: Generating in-the-wild 3D Assets in Latent Space
Ze Yang, Jingkang Wang, Haowei Zhang +3
High-quality 3D assets for traffic participants are critical for multi-sensor simulation, which is essential for the safe end-to-end development of autonomy. Building assets from i…
Efficient Equivariant Transformer for Self-Driving Agent Modeling
Scott Xu, Dian Chen, Kelvin Wong +3
Accurately modeling agent behaviors is an important task in self-driving. It is also a task with many symmetries, such as equivariance to the order of agents and objects in the sce…
FOMO-3D: Using Vision Foundation Models for Long-Tailed 3D Object Detection
Anqi Joyce Yang, James Tu, Nikita Dvornik +2
In order to navigate complex traffic environments, self-driving vehicles must recognize many semantic classes pertaining to vulnerable road users or traffic control devices. Howeve…