Hyperrealistic 3D Dental Model Texturing for Synthetic Training Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The quality of dental analysis using neural networks is hindered by the limited availability and confidentiality of dental images, leading to suboptimal training data and potential human errors in labeling, which degrades the analysis accuracy.
Innovation Solution
A method to enrich the training data by creating hyperrealistic views from dental models, using neural networks to transform 3D models into photo-like representations, thereby generating high-quality training data without human intervention, and simulating rare dental scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real dental images are used for training, then the training data is authentic and realistic, but the availability is limited and confidentiality issues arise
Solution Approach 1:
The patent creates synthetic 3D dental models that copy and replicate the characteristics of real dental structures. These virtual models serve as substitutes for real patient images, allowing unlimited generation of training data while maintaining anatomical accuracy and avoiding confidentiality issues.
Solution Approach 2:
The system uses automated algorithms to generate and label training data without requiring human operators to manually annotate each image. The neural network and processing circuits automatically create hyperrealistic views and generate corresponding labels, eliminating manual labor and enabling large-scale data generation.
2Quantity of substance
If manual labeling is performed by operators, then the training data can be created, but human errors and inconsistent interpretations degrade the quality
Solution Approach 1:
The system implements automated labeling through neural networks and processing circuits that generate labels algorithmically. This eliminates human operators from the labeling process, ensuring consistent, error-free labels while maintaining high throughput for creating large training datasets.
Solution Approach 2:
The patent replaces the mechanical process of manual labeling with an automated computational system. Neural networks and processing circuits substitute human operators, using algorithmic methods to generate precise and consistent labels without human error or interpretation variability.
3Measurement precision
If more training records are created to improve analysis quality, then the neural network performance improves, but the process becomes more complex and time-consuming
Solution Approach 1:
The automated system generates and labels training records without human intervention, enabling rapid creation of large datasets. The neural network and processing circuits work autonomously to produce thousands of training records, improving analysis accuracy while maintaining a streamlined process.
Solution Approach 2:
The patent pre-generates diverse 3D dental models with various pathologies and conditions before they are needed for training. This preliminary creation of comprehensive training data allows the system to handle rare pathologies and diverse cases without requiring complex on-demand generation processes.
Data Source
Figure 1~3
Figure 4~8
Figure 9~10b
AI summary
A texturing method for making a three-dimensional digital "original" model hyperrealistic, said method comprising the following steps: 21') creation of a "texturing" training set; 22') training at least one "texturing" neural network, using the texturing training set, so that it learns to realistically texture an initially untextured model; 23') submitting the original model to said at least one texturing neural network, so that it textures the original model to make it hyperrealistic.