activity
20172022
most citedPoint2Node: Correlation Learning of Dynamic-Node for Point Cloud Feature Modeling

8 citations · 22 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CV20224 cited

LiDARCap: Long-range Marker-less 3D Human Motion Capture with LiDAR Point Clouds

Jialian Li, Jingyi Zhang, Zhiyong Wang +6

Existing motion capture datasets are largely short-range and cannot yet fit the need of long-range applications. We propose LiDARHuman26M, a new human motion capture dataset captur…

cs.LG2021

Federated Learning with Fair Averaging

Zheng Wang, Xiaoliang Fan, Jianzhong Qi +3

Fairness has emerged as a critical problem in federated learning (FL). In this work, we identify a cause of unfairness in FL -- conflicting gradients with large differences in the…

cs.CV20198 cited

Point2Node: Correlation Learning of Dynamic-Node for Point Cloud Feature Modeling

Wenkai Han, Chenglu Wen, Cheng Wang +2

Fully exploring correlation among points in point clouds is essential for their feature modeling. This paper presents a novel end-to-end graph model, named Point2Node, to represent…

cs.CV20196 cited

RF-Net: An End-to-End Image Matching Network based on Receptive Field

Xuelun Shen, Cheng Wang, Xin Li +5

This paper proposes a new end-to-end trainable matching network based on receptive field, RF-Net, to compute sparse correspondence between images. Building end-to-end trainable mat…

cs.CV2019

LO-Net: Deep Real-time Lidar Odometry

Qing Li, Shaoyang Chen, Cheng Wang +4

We present a novel deep convolutional network pipeline, LO-Net, for real-time lidar odometry estimation. Unlike most existing lidar odometry (LO) estimations that go through indivi…

cs.CV20174 cited

Traffic Sign Timely Visual Recognizability Evaluation Based on 3D Measurable Point Clouds

Shanxin Zhang, Cheng Wang, Zhuang Yang +3

The timely provision of traffic sign information to drivers is essential for the drivers to respond, to ensure safe driving, and to avoid traffic accidents in a timely manner. We p…