期刊文献+

一种探讨点云深度学习决策的PointNet++解析网络 被引量:4

A PointNet++ Analytic Network that Explores Point Cloud Deep Learning Decision-Making
原文传递
导出
摘要 针对三维点云数据分类深度学习可解释性研究,提出一种探讨点云深度学习决策的PointNet++解析网络,探索隐藏在PointNet++网络中的特征信息。根据二维图像解译工作中的类激活映射图,提出了三维点云的类激活映射图,并将点云类激活映射图作为探索PointNet++网络分类决策的依据,采用多层感知机取代全连接层,并使用均值池化层来聚合卷积特征。实验数据为ModelNet40数据集,验证了所提出的PointNet++解析网络的可行性。研究结果表明,所提算法达到了较高的分类精度并且能够对PointNet++分类决策进行探讨,提取直接有助于决策制定的特征区域。 Aiming at the interpretability of 3D point cloud data classification deep learning,this paper proposes a PointNet++ analytic network that explores point cloud deep learning decision-making,and explores the feature information hidden in the PointNet++ network.According to the class activation map in the 2D image interpretation work,the class activation map of the 3D point cloud is proposed,and the point cloud class activation map is used as the basis for exploring the classification decision of the PointNet++ network,and the multi-layer perceptron is used to replace the full connection Layers and use mean pooling layers to aggregate convolutional features.The experimental data is the ModelNet40 data set,which verifies the feasibility of the proposed PointNet++ parsing network.The research results show that the proposed algorithm achieves high classification accuracy and can discuss PointNet ++ classification decision-making,and extract feature areas that directly contribute to decision-making.
作者 龚国栋 李耀斌 花向红 赵不钒 卢荣 GONG Guodong;LI Yaobin;HUA Xianghong;ZHAO Bufan;LU Rong(Guangzhou Urban planning&Design Survey Research Institute,Guangzhou 510030,China;School of Geodesy&Geomatics,Wuhan University,Wuhan 430079,Chin;Hazard Monitoring&Prevention Research Center,Wuhan University,Wuhan 430079,Chin)
出处 《测绘地理信息》 CSCD 2022年第6期50-54,共5页 Journal of Geomatics
基金 国家自然科学基金(41674005,41871373)。
关键词 点云 深度学习 PointNet++ 解析网络 point cloud deep learning PointNet++ analytic network
  • 引文网络
  • 相关文献

参考文献4

二级参考文献8

共引文献81

同被引文献39

引证文献4

二级引证文献1

;
使用帮助 返回顶部