activity
20172021
most citedGMAN: A Graph Multi-Attention Network for Traffic Prediction

87 citations · 137 across the 9 of their papers we have counts for

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10 papers · 1 filter

cs.CV20217 cited

DLA-Net: Learning Dual Local Attention Features for Semantic Segmentation of Large-Scale Building Facade Point Clouds

Yanfei Su, Weiquan Liu, Zhimin Yuan +4

Semantic segmentation of building facade is significant in various applications, such as urban building reconstruction and damage assessment. As there is a lack of 3D point clouds…

cs.CV2020

VPC-Net: Completion of 3D Vehicles from MLS Point Clouds

Yan Xia, Yusheng Xu, Cheng Wang +1

As a dynamic and essential component in the road environment of urban scenarios, vehicles are the most popular investigation targets. To monitor their behavior and extract their ge…

cs.CV20204 cited

Review: deep learning on 3D point clouds

Saifullahi Aminu Bello, Shangshu Yu, Cheng Wang

Point cloud is point sets defined in 3D metric space. Point cloud has become one of the most significant data format for 3D representation. Its gaining increased popularity as a re…

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

PointNLM: Point Nonlocal-Means for vegetation segmentation based on middle echo point clouds

Jonathan Li, Rongren Wu, Yiping Chen +3

Middle-echo, which covers one or a few corresponding points, is a specific type of 3D point cloud acquired by a multi-echo laser scanner. In this paper, we propose a novel approach…