79 citations · 117 across the 14 of their papers we have counts for
11 papers · 1 filter
Self-Supervised Instance Segmentation by Grasping
YuXuan Liu, Xi Chen, Pieter Abbeel
Instance segmentation is a fundamental skill for many robotic applications. We propose a self-supervised method that uses grasp interactions to collect segmentation supervision for…
Distributional Instance Segmentation: Modeling Uncertainty and High Confidence Predictions with Latent-MaskRCNN
YuXuan Liu, Nikhil Mishra, Pieter Abbeel +1
Object recognition and instance segmentation are fundamental skills in any robotic or autonomous system. Existing state-of-the-art methods are often unable to capture meaningful un…
FSNet: Redesign Self-Supervised MonoDepth for Full-Scale Depth Prediction for Autonomous Driving
Yuxuan Liu, Zhenhua Xu, Huaiyang Huang +2
Predicting accurate depth with monocular images is important for low-cost robotic applications and autonomous driving. This study proposes a comprehensive self-supervised framework…
Autoregressive Uncertainty Modeling for 3D Bounding Box Prediction
YuXuan Liu, Nikhil Mishra, Maximilian Sieb +3
3D bounding boxes are a widespread intermediate representation in many computer vision applications. However, predicting them is a challenging task, largely due to partial observab…
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…
csBoundary: City-scale Road-boundary Detection in Aerial Images for High-definition Maps
Zhenhua Xu, Yuxuan Liu, Lu Gan +4
High-Definition (HD) maps can provide precise geometric and semantic information of static traffic environments for autonomous driving. Road-boundary is one of the most important i…
YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection
Yuxuan Liu, Lujia Wang, Ming Liu
Object detection in 3D with stereo cameras is an important problem in computer vision, and is particularly crucial in low-cost autonomous mobile robots without LiDARs. Nowadays, mo…
Ground-aware Monocular 3D Object Detection for Autonomous Driving
Yuxuan Liu, Yuan Yixuan, Ming Liu
Estimating the 3D position and orientation of objects in the environment with a single RGB camera is a critical and challenging task for low-cost urban autonomous driving and mobil…
ATG-PVD: Ticketing Parking Violations on A Drone
Hengli Wang, Yuxuan Liu, Huaiyang Huang +8
In this paper, we introduce a novel suspect-and-investigate framework, which can be easily embedded in a drone for automated parking violation detection (PVD). Our proposed framewo…