6 papers
Test-time Correction: An Online 3D Detection System via Visual Prompting
Hanxue Zhang, Zetong Yang, Yanan Sun +4
This paper introduces Test-time Correction (TTC), an online 3D detection system designed to rectify test-time errors using various auxiliary feedback, aiming to enhance the safety…
Detect Anything 3D in the Wild
Hanxue Zhang, Haoran Jiang, Qingsong Yao +6
Despite the success of deep learning in close-set 3D object detection, existing approaches struggle with zero-shot generalization to novel objects and camera configurations. We int…
ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models
Shadi Hamdan, Chonghao Sima, Zetong Yang +2
How can we benefit from large models without sacrificing inference speed, a common dilemma in self-driving systems? A prevalent solution is a dual-system architecture, employing a…
Decoupled Diffusion Sparks Adaptive Scene Generation
Yunsong Zhou, Naisheng Ye, William Ljungbergh +6
Controllable scene generation could reduce the cost of diverse data collection substantially for autonomous driving. Prior works formulate the traffic layout generation as predicti…
NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking
Daniel Dauner, Marcel Hallgarten, Tianyu Li +9
Benchmarking vision-based driving policies is challenging. On one hand, open-loop evaluation with real data is easy, but these results do not reflect closed-loop performance. On th…
Fully Sparse 3D Occupancy Prediction
Haisong Liu, Yang Chen, Haiguang Wang +6
Occupancy prediction plays a pivotal role in autonomous driving. Previous methods typically construct dense 3D volumes, neglecting the inherent sparsity of the scene and suffering…