Automatic Point Cloud Patching of Intraoral Three-Dimensional Scanning Based on Deep Learning
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Published on: August 5, 2021
Qianhan Zheng1, Yimin Wang2, Mengqi Zhou1
1Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, Zhejiang, China.
Introduction And Aims:
Intraoral scanning (IOS) captures real-time surface morphology of teeth and is widely used in clinical dentistry. However, due to the complex intraoral environment, data loss during scanning is common, leading to incomplete three-dimensional (3D) point clouds. This study aimed to develop and evaluate a deep learning-based method to automatically restore missing regions in intraoral 3D point clouds, thereby improving the accuracy and efficiency of digital orthodontic workflows.
Methods:
A Point Fractal Network architecture was adopted to reconstruct incomplete IOS data. A dataset comprising 314 IOS scans and 4162 individual teeth was used for training and validation. Missing data were simulated by removing random portions of point clouds (5%, 10%, 15%, and 20%). Model performance was assessed using Chamfer distance (CD) to measure the accuracy of point cloud completion across different levels of data loss.
Results:
The proposed method achieves robust performance, maintaining average CD values below 0.01 across most levels of simulated data loss. Visual comparisons confirmed high geometric fidelity between the completed and original point clouds. Furthermore, the model demonstrated efficient processing, completing each point cloud in approximately 0.5 seconds, enabling near real-time restoration during clinical scanning.
Conclusion:
The deep learning-based model accurately restores missing IOS data, improving the precision and efficiency of digital dental workflows. Its speed and accuracy support real-time clinical applications and reduce reliance on manual corrections.
Clinical Relevance:
This method improves clinical efficiency, reduces chairside time, and enhances both patient comfort and treatment acceptance. In addition, it minimises human error and increases the precision of dental restorations. As digital dentistry continues to evolve, this approach holds great potential for improving the accuracy and efficiency of dental treatments, paving the way for broader artificial intelligence integration in clinical practice.
