@inproceedings{2f23a4e6d67c4b39bbb93e25fdac540d,
title = "Clustering personalized 3D printing models with multiple modal CNN",
abstract = "Clustering personalized 3D printing models is very useful for a cloud manufacturing management system, but it is difficult to cluster directly because of the complexity and abstraction of the 3D print model input. In this paper we use the convolution neural networks (CNNs) to learn the similarities of 3D print model pairs in different input modes and integrate these similarities by multi-channel spectral clustering. The three-dimensional CNN and the view-based CNN are used for different input modes. Our experiments show that the accuracy of the clustering can be improved by merging training results of different input modes.",
keywords = "CNNs, Personalized 3D print model, Similarity classifier, Spectral cluster",
author = "Jianwei Chen and Lin Zhang and Xinyu Dong",
note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Singapore Pte Ltd.; Chinese Intelligent Systems Conference, CISC 2017 ; Conference date: 14-10-2017 Through 15-10-2017",
year = "2018",
doi = "10.1007/978-981-10-6496-8\_64",
language = "英语",
isbn = "9789811064951",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Verlag",
pages = "703--712",
editor = "Junping Du and Weicun Zhang and Yingmin Jia",
booktitle = "Proceedings of 2017 Chinese Intelligent Systems Conference",
address = "德国",
}