Affinity-Point Graph Convolutional Network for 3D Point Cloud Analysis
Efficient learning of 3D shape representation from point cloud is one of the biggest requirements in 3D computer vision. In recent years, convolutional neural networks have achieved great success in 2D image representation learning. However, unlike images that have a Euclidean structure, 3D point cl...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-05-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/11/5328 |
| _version_ | 1797494339617685504 |
|---|---|
| author | Yang Wang Shunping Xiao |
| author_facet | Yang Wang Shunping Xiao |
| author_sort | Yang Wang |
| collection | DOAJ |
| description | Efficient learning of 3D shape representation from point cloud is one of the biggest requirements in 3D computer vision. In recent years, convolutional neural networks have achieved great success in 2D image representation learning. However, unlike images that have a Euclidean structure, 3D point clouds are irregular since the neighbors of each node are inconsistent. Many studies have tried to develop various convolutional graph neural networks to overcome this problem and to achieve great results. Nevertheless, these studies simply took the centroid point and its corresponding neighbors as the graph structure, thus ignoring the structural information. In this paper, an Affinity-Point Graph Convolutional Network (AP-GCN) is proposed to learn the graph structure for each reference point. In this method, the affinity between points is first defined using the feature of each point feature. Then, a graph with affinity information is built. After that, the edge-conditioned convolution is performed between the graph vertices and edges to obtain stronger neighborhood information. Finally, the learned information is used for recognition and segmentation tasks. Comprehensive experiments demonstrate that AP-GCN learned much more reasonable features and achieved significant improvements in 3D computer vision tasks such as object classification and segmentation. |
| first_indexed | 2024-03-10T01:32:51Z |
| format | Article |
| id | doaj.art-afec0504be5947b587b7ce44d1a6a96c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T01:32:51Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-afec0504be5947b587b7ce44d1a6a96c2023-11-23T13:39:51ZengMDPI AGApplied Sciences2076-34172022-05-011211532810.3390/app12115328Affinity-Point Graph Convolutional Network for 3D Point Cloud AnalysisYang Wang0Shunping Xiao1College of Electronic Science, National University of Defense Technology, Changsha 410073, ChinaCollege of Electronic Science, National University of Defense Technology, Changsha 410073, ChinaEfficient learning of 3D shape representation from point cloud is one of the biggest requirements in 3D computer vision. In recent years, convolutional neural networks have achieved great success in 2D image representation learning. However, unlike images that have a Euclidean structure, 3D point clouds are irregular since the neighbors of each node are inconsistent. Many studies have tried to develop various convolutional graph neural networks to overcome this problem and to achieve great results. Nevertheless, these studies simply took the centroid point and its corresponding neighbors as the graph structure, thus ignoring the structural information. In this paper, an Affinity-Point Graph Convolutional Network (AP-GCN) is proposed to learn the graph structure for each reference point. In this method, the affinity between points is first defined using the feature of each point feature. Then, a graph with affinity information is built. After that, the edge-conditioned convolution is performed between the graph vertices and edges to obtain stronger neighborhood information. Finally, the learned information is used for recognition and segmentation tasks. Comprehensive experiments demonstrate that AP-GCN learned much more reasonable features and achieved significant improvements in 3D computer vision tasks such as object classification and segmentation.https://www.mdpi.com/2076-3417/12/11/53283D point cloud analysisdeep learninggraph convolution network3D classificationsemantic segmentation |
| spellingShingle | Yang Wang Shunping Xiao Affinity-Point Graph Convolutional Network for 3D Point Cloud Analysis Applied Sciences 3D point cloud analysis deep learning graph convolution network 3D classification semantic segmentation |
| title | Affinity-Point Graph Convolutional Network for 3D Point Cloud Analysis |
| title_full | Affinity-Point Graph Convolutional Network for 3D Point Cloud Analysis |
| title_fullStr | Affinity-Point Graph Convolutional Network for 3D Point Cloud Analysis |
| title_full_unstemmed | Affinity-Point Graph Convolutional Network for 3D Point Cloud Analysis |
| title_short | Affinity-Point Graph Convolutional Network for 3D Point Cloud Analysis |
| title_sort | affinity point graph convolutional network for 3d point cloud analysis |
| topic | 3D point cloud analysis deep learning graph convolution network 3D classification semantic segmentation |
| url | https://www.mdpi.com/2076-3417/12/11/5328 |
| work_keys_str_mv | AT yangwang affinitypointgraphconvolutionalnetworkfor3dpointcloudanalysis AT shunpingxiao affinitypointgraphconvolutionalnetworkfor3dpointcloudanalysis |