Zonglin Meng — accepted AI-conference papers

2024

V2X-Real: a Largs-Scale Dataset for Vehicle-to-Everything Cooperative Perception

Hao Xiang, Xin Xia, Zhaoliang Zheng, Runsheng Xu, Letian Gao, Zewei Zhou +12

ECCV 2024poster

"Recent advancements in Vehicle-to-Everything (V2X) technologies have enabled autonomous vehicles to share sensing information to see through occlusions, greatly boosting the perception capability. However, there are no real-world datasets to facilitate the real V2X cooperative perception research –…

Cited by 33 Source ↗PDF ↗code ↗Scholar ↗

2023

V2V4Real: A Real-World Large-Scale Dataset for Vehicle-to-Vehicle Cooperative Perception

Runsheng Xu, Xin Xia, Jinlong Li, Hanzhao Li, Shuo Zhang, Zhengzhong Tu +7

CVPR 2023highlight

Modern perception systems of autonomous vehicles are known to be sensitive to occlusions and lack the capability of long perceiving range. It has been one of the key bottlenecks that prevents Level 5 autonomy. Recent research has demonstrated that the Vehicle-to-Vehicle (V2V) cooperative perception…

Cited by 180 Source ↗PDF ↗code ↗Scholar ↗