activity
20182025
most citedA Review of Safe Reinforcement Learning: Methods, Theory and Applications

102 citations · 292 across the 18 of their papers we have counts for

collaborators
Showing 2020Show all

5 papers · 1 filter

cs.CV2020★ 1 cited

PointINet: Point Cloud Frame Interpolation Network

Fan Lu, Guang Chen, Sanqing Qu +3

LiDAR point cloud streams are usually sparse in time dimension, which is limited by hardware performance. Generally, the frame rates of mechanical LiDAR sensors are 10 to 20 Hz, wh…

cs.CV2020★ 3 cited

MoNet: Motion-based Point Cloud Prediction Network

Fan Lu, Guang Chen, Yinlong Liu +3

Predicting the future can significantly improve the safety of intelligent vehicles, which is a key component in autonomous driving. 3D point clouds accurately model 3D information…

cs.CV2020★ 12 cited

LAP-Net: Adaptive Features Sampling via Learning Action Progression for Online Action Detection

Sanqing Qu, Guang Chen, Dan Xu +3

Online action detection is a task with the aim of identifying ongoing actions from streaming videos without any side information or access to future frames. Recent methods proposed…

cs.CV2020★ 29 cited

RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor

Fan Lu, Guang Chen, Yinlong Liu +2

Keypoint detector and descriptor are two main components of point cloud registration. Previous learning-based keypoint detectors rely on saliency estimation for each point or farth…

cs.NE2020★ 53 cited

Indirect and Direct Training of Spiking Neural Networks for End-to-End Control of a Lane-Keeping Vehicle

Zhenshan Bing, Claus Meschede, Guang Chen +2

Building spiking neural networks (SNNs) based on biological synaptic plasticities holds a promising potential for accomplishing fast and energy-efficient computing, which is benefi…