5 citations · 13 across the 4 of their papers we have counts for
6 papers
DetMatch: Two Teachers are Better Than One for Joint 2D and 3D Semi-Supervised Object Detection
Jinhyung Park, Chenfeng Xu, Yiyang Zhou +2
While numerous 3D detection works leverage the complementary relationship between RGB images and point clouds, developments in the broader framework of semi-supervised object recog…
Automatic Construction of Lane-level HD Maps for Urban Scenes
Yiyang Zhou, Yuichi Takeda, Masayoshi Tomizuka +1
High definition (HD) maps have demonstrated their essential roles in enabling full autonomy, especially in complex urban scenarios. As a crucial layer of the HD map, lane-level map…
A Simple and Efficient Multi-task Network for 3D Object Detection and Road Understanding
Di Feng, Yiyang Zhou, Chenfeng Xu +2
Detecting dynamic objects and predicting static road information such as drivable areas and ground heights are crucial for safe autonomous driving. Previous works studied each perc…
Labels Are Not Perfect: Inferring Spatial Uncertainty in Object Detection
Di Feng, Zining Wang, Yiyang Zhou +5
The availability of many real-world driving datasets is a key reason behind the recent progress of object detection algorithms in autonomous driving. However, there exist ambiguity…
Inferring Spatial Uncertainty in Object Detection
Zining Wang, Di Feng, Yiyang Zhou +5
The availability of real-world datasets is the prerequisite for developing object detection methods for autonomous driving. While ambiguity exists in object labels due to error-pro…
UrbanLoco: A Full Sensor Suite Dataset for Mapping and Localization in Urban Scenes
Weisong Wen, Yiyang Zhou, Guohao Zhang +5
Mapping and localization is a critical module of autonomous driving, and significant achievements have been reached in this field. Beyond Global Navigation Satellite System (GNSS),…