activity
20172023
most citedSR-LSTM: State Refinement for LSTM towards Pedestrian Trajectory Prediction

33 citations · 102 across the 15 of their papers we have counts for

collaborators

31 papers

cs.RO2023

InteractionNet: Joint Planning and Prediction for Autonomous Driving with Transformers

Jiawei Fu, Yanqing Shen, Zhiqiang Jian +3

Planning and prediction are two important modules of autonomous driving and have experienced tremendous advancement recently. Nevertheless, most existing methods regard planning an…

cs.CV2023

Complementing Onboard Sensors with Satellite Map: A New Perspective for HD Map Construction

Wenjie Gao, Jiawei Fu, Yanqing Shen +3

High-definition (HD) maps play a crucial role in autonomous driving systems. Recent methods have attempted to construct HD maps in real-time using vehicle onboard sensors. Due to t…

cs.CV2023

FS-Depth: Focal-and-Scale Depth Estimation from a Single Image in Unseen Indoor Scene

Chengrui Wei, Meng Yang, Lei He +1

It has long been an ill-posed problem to predict absolute depth maps from single images in real (unseen) indoor scenes. We observe that it is essentially due to not only the scale-…

cs.CV20225 cited

Using Detection, Tracking and Prediction in Visual SLAM to Achieve Real-time Semantic Mapping of Dynamic Scenarios

Xingyu Chen, Jianru Xue, Jianwu Fang +2

In this paper, we propose a lightweight system, RDS-SLAM, based on ORB-SLAM2, which can accurately estimate poses and build semantic maps at object level for dynamic scenarios in r…

cs.CV20224 cited

Trajectory Forecasting from Detection with Uncertainty-Aware Motion Encoding

Pu Zhang, Lei Bai, Jianru Xue +3

Trajectory forecasting is critical for autonomous platforms to make safe planning and actions. Currently, most trajectory forecasting methods assume that object trajectories have b…

cs.CV20213 cited

Multi-Scale Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition

Pengfei Zhang, Cuiling Lan, Wenjun Zeng +3

Skeleton data is of low dimension. However, there is a trend of using very deep and complicated feedforward neural networks to model the skeleton sequence without considering the c…