Qihang Fang
Biographic Data
| ID | 10055484 |
|---|---|
| NAME | Qihang Fang |
| GIVEN NAMES | Qihang |
| FAMILY NAME | Fang |
| SIGNATURE | FANG Q |
| AFFILIATIONS | Chinese Academy of Sciences |
| ORCID | 0000-0003-1438-0094 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
Enlarge the Error Prediction Dataset in 3-D Printing
The quality of the dataset is critical to the performance of neural networks for error prediction in 3-D printing. In order to enlarge the dataset, we propose a customized two-stage framework, cascaded cross-modality generative adversarial networks (CCMGANs), for generating dental crown meshes in an unsupervised manner. At the first stage, a displacement map-guided generative adversarial network (GAN) is used to generate coarse meshes with divers…
Geometry-Guided Neural Implicit Surface Reconstruction
Multiview 3-D reconstruction holds considerable promise across a wide applications in social manufacturing. Conducting in-depth research on precise and robust multiview 3-D reconstruction holds the potential to significantly empower the domain of social manufacturing. Recently, there has been a burgeoning interest in the domain of neural implicit surfaces learning through volume rendering for the purpose of multiview reconstruction without 3-D su…
A Dual Neural Network for Defect Detection With Highly Imbalanced Data in 3-D Printing
Digital light processing (DLP) is a popular additive manufacturing technology that uses light irradiation to fabricate 3-D devices via a projector to achieve laser-sensitive resin curing. However, the performance and reliability of DLP can be affected by internal defects such as printing errors and the accumulation of residual stress. Existing defect detection methods rely on monitoring the printed parts, which leads to resource wastage and strug…
No prominent works on this page.
Enlarge the Error Prediction Dataset in 3-D Printing
The quality of the dataset is critical to the performance of neural networks for error prediction in 3-D printing. In order to enlarge the dataset, we propose a customized two-stage framework, cascaded cross-modality generative adversarial networks (CCMGANs), for generating dental crown meshes in an unsupervised manner. At the first stage, a displacement map-guided generative adversarial network (GAN) is used to generate coarse meshes with divers…
Geometry-Guided Neural Implicit Surface Reconstruction
Multiview 3-D reconstruction holds considerable promise across a wide applications in social manufacturing. Conducting in-depth research on precise and robust multiview 3-D reconstruction holds the potential to significantly empower the domain of social manufacturing. Recently, there has been a burgeoning interest in the domain of neural implicit surfaces learning through volume rendering for the purpose of multiview reconstruction without 3-D su…
A Dual Neural Network for Defect Detection With Highly Imbalanced Data in 3-D Printing
Digital light processing (DLP) is a popular additive manufacturing technology that uses light irradiation to fabricate 3-D devices via a projector to achieve laser-sensitive resin curing. However, the performance and reliability of DLP can be affected by internal defects such as printing errors and the accumulation of residual stress. Existing defect detection methods rely on monitoring the printed parts, which leads to resource wastage and strug…
Artificial Intelligence (3 works) · Computer Science (3 works) · Artificial neural network (2 works) · Manufacturing Process and Optimization (2 works) · 3D Shape Modeling and Analysis (1 work) · Advanced Numerical Analysis Techniques (1 work) · Algorithm (1 work) · Data mining (1 work) · Dental materials and restorations (1 work) · Dental Radiography and Imaging (1 work)