.png){kind=icon}
返回
3D Shape Classification Using a Single View
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2020.3035583.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
View-based 3D shape classification is widely used in machine vision, information retrieval and other fields. However, there are two problems in current methods. First, current 3D shape classifiers fail to make good use of pose information of 3D shapes. Secondly, many views are required to obtain good classification accuracy, which leads to low efficiency. In order to solve these problems, we propose a novel 3D shape classification method based on Convolutional Neural Network (CNN). In the training stage, this method first learns a CNN to extract features, and then uses features of views from different viewpoint groups to train six 3D shape classifiers which fully mine the pose information of 3D shapes. Meanwhile, an additional class is adopted to improve the discrimination of 3D shape classifiers. In the recognition stage, the weighted fusion of image clarity evaluation functions is used to select the most representative view for the 3D shape recognition. Experiments on the ModelNet10 and ModelNet40 show that the classification accuracy of the proposed method can reach up to 91.18% and 89.01% when only using a single view and the efficiency is improved substantially.
Keyword:
Shape classification
convolutional neural network
3D shape classifier
representative view
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
暂无机构信息
引用论文
Artificial Microfossils: Experimental Studies of Permineralization of Blue-Green Algae in Silica.png)
Science
ScienceIF0
PREAI
分享收藏Molecular characterization, tissue distribution and expression analysis of Interleukin-12 receptor β2 chain in sheepGene
GeneIF0
PREAI
分享收藏
查看更多内容