154 citations · 712 across the 31 of their papers we have counts for
4 papers · 1 filter
RNGDet++: Road Network Graph Detection by Transformer with Instance Segmentation and Multi-scale Features Enhancement
Zhenhua Xu, Yuxuan Liu, Yuxiang Sun +2
The road network graph is a critical component for downstream tasks in autonomous driving, such as global route planning and navigation. In the past years, road network graphs are…
CenterLineDet: CenterLine Graph Detection for Road Lanes with Vehicle-mounted Sensors by Transformer for HD Map Generation
Zhenhua Xu, Yuxuan Liu, Yuxiang Sun +2
With the fast development of autonomous driving technologies, there is an increasing demand for high-definition (HD) maps, which provide reliable and robust prior information about…
RNGDet: Road Network Graph Detection by Transformer in Aerial Images
Zhenhua Xu, Yuxuan Liu, Lu Gan +4
Road network graphs provide critical information for autonomous-vehicle applications, such as drivable areas that can be used for motion planning algorithms. To find road network g…
Why-So-Deep: Towards Boosting Previously Trained Models for Visual Place Recognition
M. Usman Maqbool Bhutta, Yuxiang Sun, Darwin Lau +1
Deep learning-based image retrieval techniques for the loop closure detection demonstrate satisfactory performance. However, it is still challenging to achieve high-level performan…