Real-Time 3D Surface Digitization

Keyword(s):  
Author(s):  
N Smith ◽  
I Meir ◽  
G Hale ◽  
R Howe ◽  
L Johnson ◽  
...  

2004 ◽  
Vol 60 (1) ◽  
pp. S603-S604 ◽  
Author(s):  
U. Johnson ◽  
D. Landau ◽  
J. Lindgren-Turner ◽  
N. Smith ◽  
I. Meir ◽  
...  

The Analyst ◽  
2016 ◽  
Vol 141 (14) ◽  
pp. 4424-4431 ◽  
Author(s):  
Yingjie Yu ◽  
Qi Zhang ◽  
Jonathan Buscaglia ◽  
Chung-Chueh Chang ◽  
Ying Liu ◽  
...  

In this study, a real time potentiometric biosensor based on the 3D surface molecular imprinting was developed for CEA detection.


Author(s):  
Liu Chenang ◽  
Wang Rongxuan ◽  
Zhenyu Kong ◽  
Babu Suresh ◽  
Joslin Chase ◽  
...  

Layer-wise 3D surface morphology information is critical for the quality monitoring and control of additive manufacturing (AM) processes. However, most of the existing 3D scan technologies are either contact or time consuming, which are not capable of obtaining the 3D surface morphology data in a real-time manner during the process. Therefore, the objective of this study is to achieve real-time 3D surface data acquisition in AM, which is achieved by a supervised deep learning-based image analysis approach. The key idea of this proposed method is to capture the correlation between 2D image and 3D point cloud, and then quantify this relationship by using a deep learning algorithm, namely, convolutional neural network (CNN). To validate the effectiveness and efficiency of the proposed method, both simulation and real-world case studies were performed. The results demonstrate that this method has strong potential to be applied for real-time surface morphology measurement in AM, as well as other advanced manufacturing processes.


Sign in / Sign up

Export Citation Format

Share Document