.png){kind=icon}
返回
Multi-view attention-convolution pooling network for 3D point cloud classification
.png){kind=icon}
OA
AI
DOI：10.1007/s10489-021-02840-2.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Classifying 3D point clouds is an important and challenging task in computer vision. Currently, classification methods using multiple views lose characteristic or detail information during the representation or processing of views. For this reason, we propose a multi-view attention-convolution pooling network framework for 3D point cloud classification tasks. This framework uses Res2Net to extract the features from multiple 2D views. Our attention-convolution pooling method finds more useful information in the input data related to the current output, effectively solving the problem of feature information loss caused by feature representation and the detail information loss during dimensionality reduction. Finally, we obtain the probability distribution of the model to be classified using a full connection layer and the softmax function. The experimental results show that our framework achieves higher classification accuracy and better performance than other contemporary methods using the ModelNet40 dataset.
Keyword:
3D point cloud
Multi-view
Attention-convolution pooling
Point cloud classification
期刊
IF:
3.5
论文数:
7.6K
被引数:
1.7W
机构
暂无机构信息
引用论文
Multi-view manifold regularized learning-based method for prioritizing candidate disease miRNAs
PREAI
分享收藏Discovery and characterization of flavonoids in vine tea as catechol-O-methyltransferase inhibitors.png)
Fitoterapia
FitoterapiaIF0
PREAI
分享收藏3D Point Cloud Classification for Autonomous Driving via Dense-Residual Fusion NetworkIEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容