9 citations · 29 across the 9 of their papers we have counts for
9 papers
Center Feature Fusion: Selective Multi-Sensor Fusion of Center-based Objects
Philip Jacobson, Yiyang Zhou, Wei Zhan +2
Leveraging multi-modal fusion, especially between camera and LiDAR, has become essential for building accurate and robust 3D object detection systems for autonomous vehicles. Until…
What Matters for 3D Scene Flow Network
Guangming Wang, Yunzhe Hu, Zhe Liu +4
3D scene flow estimation from point clouds is a low-level 3D motion perception task in computer vision. Flow embedding is a commonly used technique in scene flow estimation, and it…
SST-Calib: Simultaneous Spatial-Temporal Parameter Calibration between LIDAR and Camera
Akio Kodaira, Yiyang Zhou, Pengwei Zang +2
With information from multiple input modalities, sensor fusion-based algorithms usually out-perform their single-modality counterparts in robotics. Camera and LIDAR, with complemen…
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),…