3D Deep Learning for 3D Printing of Tooth Model

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 p...

Full description

Saved in:
Bibliographic Details
Published in2019 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI) pp. 274 - 279
Main Authors Liu, Yiwen, Shang, Xiuqin, Shen, Zhen, Hu, Bin, Wang, Zhihai, Xiong, Gang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2019
Subjects
Cameras
CNN
Deep learning
Image segmentation
Object segmentation
point cloud
semantic segmentation
Sensors
Three-dimensional displays
Transforms
Online AccessGet full text
DOI10.1109/SOLI48380.2019.8955074

Cover

More Information
Summary: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.
DOI:10.1109/SOLI48380.2019.8955074