3D Deep Learning for 3D Printing of Tooth Model - 文章详情页

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Recently,　with　the　development　of　both　3D　sensors　and　3D　virtual　network　that　bring　the　needs　of　interaction　with　the　real　world,　many　3D　applications　burst　out.　However,　it　is　difficult　to　understanding　these　three-dimensional　scenes　with　a　fixed　program.　Then,　a　data-driven　method　is　required　to　process　these　3D　data,　which　brings　a　strong　demand　of　3D　Deep　Learning　in　3D　data.　Towards　this　goal,　with　an　end-to-end　deep　learning,　the　experiment　is　based　on　PointNet++,　a　well　proposed　method　for　feature　extraction.　The　experiment　optimizes　the　network　structure　and　parameters　to　improve　the　classification　results.　Finally,　the　network　is　applied　to　tooth　model　for　classification　and　identification　so　that　the　dental　model　can　be　found　from　different　perspectives.　©　2019　IEEE.

关键词：

Learning systems Semantics 3D printers Deep learning

作者机构：

[ 1 ] [Liu, Yiwen]State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing University of Technology, Faculty of Information, Beijing, China
[ 2 ] [Shang, Xiuqin]State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing University of Technology, Faculty of Information, Beijing, China
[ 3 ] [Shen, Zhen]Beijing Engineering Research Center of Intelligent Systems and Technology, Chinese Academy of Sciences, Beijing, China
[ 4 ] [Hu, Bin]Beijing Engineering Research Center of Intelligent Systems and Technology, Chinese Academy of Sciences, Beijing, China
[ 5 ] [Wang, Zhihai]Beijing University of Technology, Faculty of Information, Beijing, China
[ 6 ] [Xiong, Gang]State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing University of Technology, Faculty of Information, Beijing, China

通讯作者信息：

[xiong, gang]state key laboratory for management and control of complex systems, institute of automation, chinese academy of sciences, beijing university of technology, faculty of information, beijing, china

电子邮件地址：

gang.xiong@ia.ac.cn

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