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A Hierarchical Graph Network for 3D Object Detection on Point Clouds
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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In this paper, we propose a new graph convolution (GConv) based hierarchical graph network (HGNet) for 3D object detection, which processes raw point clouds directly to predict 3D bounding boxes. ...
An SA-GConv based U-shape network captures the multi-level features, which are mapped into an identical feature space by an improved voting module and then further utilized to generate proposals. ...
In this paper, we propose a novel Hierarchical Graph Network (HGNet) for 3D object detection on point clouds, based on graph convolutions (GConvs). ...
doi:10.1109/cvpr42600.2020.00047
dblp:conf/cvpr/ChenLSYCW20
fatcat:6imh4oabfjdqrdwkddb6sxljqy
Table of Contents
2022
IEEE Transactions on Industrial Informatics
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Li 437 Multilevel Attention Based U-Shape Graph Neural Network for Point Clouds Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
Ghosh 207 A Remote Estimation Method of Smart Meter Errors Based on Neural Network Filter and Generalized Damping Recursive Least Square . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
doi:10.1109/tii.2021.3113150
fatcat:h3dbl4itlrcophunkw4jstg44y
Graph convolutional networks: a comprehensive review
2019
Computational Social Networks
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Deep learning models on graphs (e.g., graph neural networks) have recently emerged in machine learning and other related areas, and demonstrated the superior performance in various problems. ...
First, we group the existing graph convolutional network models into two categories based on the types of convolutions and highlight some graph convolutional network models in details. ...
Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on. ...
doi:10.1186/s40649-019-0069-y
fatcat:usvlugxj6jcrzesm7dthrecp3m
Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysis
[article]
2022
arXiv
pre-print
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Therefore, this work aims to conduct a comprehensive survey on various methods, including point-based, convolution-based, graph-based, and generative model-based approaches, etc. ...
The progress of deep learning (DL) has impressively improved the capability and robustness of point cloud completion. ...
Input Points Graph Attention-assisted GCN Furthermore, the attentional mechanism is also introduced into GCN. To recover finer shapes, Wu et al. [71] introduced a learning-based method. ...
arXiv:2203.03311v2
fatcat:e2kvryolufearetp4ujlw2gwwy
A Deep Neural Network Using Double Self-Attention Mechanism for ALS point cloud segmentation
2022
IEEE Access
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PointNet++ is a well known end-to-end learning network for point cloud segmentation without fully exploring the local and contextual features, which are less efficient and accurate in capturing the complexity ...
The improved local feature aggregation module can merge the deep feature of the point cloud, combining local and global self-attention convolutional networks. ...
in the graph convolutional neural network. ...
doi:10.1109/access.2022.3158438
fatcat:hjgwvuknmzghtf7vmj5ruonmoy
2021 Index IEEE Transactions on Multimedia Vol. 23
2021
IEEE transactions on multimedia
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
., +, TMM 2021 228-242 HAPGN: Hierarchical Attentive Pooling Graph Network for Point Cloud Segmentation. ...
., +, TMM 2021 3124-3136 HAPGN: Hierarchical Attentive Pooling Graph Network for Point Cloud Segmentation. ...
doi:10.1109/tmm.2022.3141947
fatcat:lil2nf3vd5ehbfgtslulu7y3lq
2020 Index IEEE Transactions on Image Processing Vol. 29
2020
IEEE Transactions on Image Processing
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., +, TIP 2020 4996-5009
Multilevel Optimization for Registration of Deformable Point Clouds. ...
., +, TIP 2020 7834-7844
Deformation
Multilevel Optimization for Registration of Deformable Point Clouds. ...
doi:10.1109/tip.2020.3046056
fatcat:24m6k2elprf2nfmucbjzhvzk3m
Table of Contents
2021
IEEE transactions on multimedia
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Feng Deep Learning for Multimedia Processing Stacked U-Shape Network With Channel-Wise Attention for Salient Object Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
Xu Image/Video/Graphics Analysis and Synthesis HAPGN: Hierarchical Attentive Pooling Graph Network for Point Cloud Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
doi:10.1109/tmm.2021.3132246
fatcat:el7u2udtybddrpbl5gxkvfricy
3D Point Cloud Descriptors in Hand-crafted and Deep Learning Age: State-of-the-Art
[article]
2020
arXiv
pre-print
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of novel 3D point cloud descriptors for accuracy of the efficiency of 3D computer vision tasks in recent years. ...
These methods can principally be divided into two categories according to the advancement of descriptors: hand-crafted based and deep learning-based apporaches, which will be further discussed from the ...
For deep learning-based descriptors, the representation of the input point cloud is crucial to the design of the neural network architecture. ...
arXiv:1802.02297v2
fatcat:jslhwu3jefb2pispewdrzyicgu
The Application of English Vocabulary Education Informatization under 5G and Cloud Computing Environment
2022
Mathematical Problems in Engineering
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In the 5G and cloud computing environment, autonomous learning based on smart mobile devices has a significant effect on improving students' vocabulary and vocabulary use. ...
This paper adopts a combination of vocabulary testing and interviews, and by building an autonomous English vocabulary learning platform based on 5G and cloud computing technology, learners can learn and ...
Reference [8] introduced a lexiconbased global semantic graph neural network (LGN) that treats each character as a node and the matched lexical information forms an edge [9] . ...
doi:10.1155/2022/9717449
fatcat:5dgund57xnatlari5bsrbf52em
IEEE Access Special Section Editorial: AI-Driven Big Data Processing: Theory, Methodology, and Applications
2020
IEEE Access
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., ''Generative adversarial network-based method for transforming single RGB image into 3-D point cloud,'' proposes a method to generate a 3-D point cloud corresponding to a single redgreen-blue (RGB) ...
Graph-based semi-supervised learning (GSSL) has attracted great attention over the past decade. ...
The authors use the single-shot refinement neural network (RefineDet) as a base network, which employs top-down architecture to offer contextual information, achieving accurate detection. ...
doi:10.1109/access.2020.3035461
fatcat:rt7ejtponrfexigie4cfpt7gd4
Table of Contents
2021
IEEE Signal Processing Letters
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Park Channel and Space Attention Neural Network for Image Denoising . . . . . . . . . . . . . . . . . ...Y. Wang, X. Song, and K. ...
MANet: Multi-Scale Attention Network for Correspondence Learning . . . . . . . . . . Y. Chen, L. Zheng, X. Liu, and G. ...
doi:10.1109/lsp.2021.3134551
fatcat:ab4b4tb5rrcu5cq6aifdekrizq
CDUNet: Cloud Detection UNet for Remote Sensing Imagery
2021
Remote Sensing
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In order to solve these problems, we propose a deep-learning model, Cloud Detection UNet (CDUNet), for cloud detection. ...
Besides, due to the lack of generalization, the traditional deep-learning network also easily loses the details and spatial information if it is directly applied to cloud detection. ...
In view of the above findings, we propose a U-shaped structure model based on the deep-learning method, which uses ResNet [15] as the backbone network. ...
doi:10.3390/rs13224533
fatcat:nfy57hb4jjhohgmsbdhwrtmt7e
2021 Index IEEE Signal Processing Letters Vol. 28
2021
IEEE Signal Processing Letters
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
., +, LSP 2021 1150-1154 GA-NET: Global Attention Network for Point Cloud Semantic Segmentation. ...
., +, LSP 2021 1230-1234 LFNet: Local Rotation Invariant Coordinate Frame for Robust Point Cloud Analysis. Cao, H., +, LSP 2021 209-213 Linguistic Steganalysis With Graph Neural Networks. ...
doi:10.1109/lsp.2022.3145253
fatcat:a3xqvok75vgepcckwnhh2mty74
Beyond single receptive field: A receptive field fusion-and-stratification network for airborne laser scanning point cloud classification
2022
ISPRS journal of photogrammetry and remote sensing (Print)
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2022 pre-print arXiv:2207.10278v1 fatcat:unmykvurznbwnombcg53jr263q
Although recent deep learning-based methods have achieved satisfactory performance, they have ignored the unicity of the receptive field, which makes the ALS point cloud classification remain challenging ...
field aggregation loss (MRFALoss) to drive the network to learn in the direction of the supervision labels with different resolutions. ...
combine global context information and local structural features and presents a graph attention convolution neural network which can be able to extract the geometric context of ALS point clouds. ...
doi:10.1016/j.isprsjprs.2022.03.019
fatcat:tnkv5uuqbngxjlqtsro5yr5a64
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