3D Dental Model Optical Parameter Estimation With Differentiable Rendering
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Solution Overview
Problem
Existing intraoral scanners struggle to provide a highly accurate and detailed representation of dental objects, particularly in terms of RGB-color, roughness, absorption, and scattering, which limits the visibility and utility of three-dimensional surface models.
Innovation Solution
A method involving a differentiable renderer that uses path tracing to iteratively adjust optical parameters such as RGB color, roughness, and scattering based on a loss function, optimizing these parameters through gradient-based optimization methods to enhance the visibility of dental objects, including internal features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional rendering methods are used to generate three-dimensional models, then the processing speed is faster, but the accuracy and realism of optical parameters (RGB color, roughness, absorption, scattering) deteriorates
Solution Approach 1:
The system performs preliminary action by pre-calculating and storing optical parameters (RGB color, roughness, absorption, scattering) during the scanning process. These pre-computed parameters are then reused during rendering, eliminating the need for time-consuming real-time optical calculations while maintaining high accuracy in the final three-dimensional model.
Solution Approach 2:
The patent replaces conventional mechanical/optical rendering systems with a computational approach using differentiable renderers and gradient-based optimization. Instead of relying on traditional ray-tracing or radiosity methods, the system uses neural networks and optimization algorithms to compute optical parameters, achieving both speed and accuracy improvements.
2Manufacturing precision
If detailed optical parameters are captured and processed, then the representation quality of dental features improves, but the computational complexity and processing time increases
Solution Approach 1:
The system segments the computational task by dividing the calculation of optical parameters into separate, independent components (RGB color, roughness, absorption, scattering). Each parameter is computed and optimized independently using dedicated neural network modules, reducing overall computational complexity while maintaining high representation quality for dental features.
Solution Approach 2:
The differentiable renderer performs self-service by automatically computing gradients and optimizing optical parameters without requiring manual intervention or complex external optimization systems. The system self-adjusts parameters through backpropagation, reducing the need for complex control mechanisms and simplifying the overall computational architecture.
3Measurement precision
If iterative optimization is performed to adjust optical parameters, then the accuracy of the three-dimensional model improves, but the processing time increases
Solution Approach 1:
The system maintains continuity of useful action by implementing gradient-based optimization that continuously refines optical parameters during the rendering process. Instead of performing discrete, time-consuming iterative adjustments, the optimization flows continuously through the differentiable renderer, efficiently converging to accurate parameter values while minimizing processing time.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting optical parameters (RGB color, roughness, absorption, scattering) during the optimization process. The differentiable renderer efficiently explores the parameter space by computing gradients with respect to each parameter, enabling rapid convergence to accurate values without requiring extensive iterative processing.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Improves the visibility and accuracy of three-dimensional dental models by refining optical parameters, allowing for better representation of surface and internal dental features, enhancing diagnostic capabilities.
Implementation Method 1
The differentiable renderer is based on path tracing that traces propagation of light through a scene modelled by rays coming from the camera until reaching a light source
Data Source
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AI summary
The present disclosure relates to a method for determining a plurality of final optical parameters of a dental object in an intraoral cavity. The method may comprise receiving a plurality of two-dimensional images of the dental object, reconstructing a three-dimensional model of the dental object based on the plurality of two-dimensional images, determining camera positions of the plurality of two-dimensional images relative to the three-dimensional model, and receiving a plurality of optical parameters. The method may further include determining in an iterative manner: a plurality of simulated two-dimensional images of the dental object by inputting the plurality of optical parameters and the camera positions to a differentiable renderer, and multiple loss-values based on a loss function between the plurality of simulated two-dimensional images and the received plurality of two-dimensional images by adjusting the plurality of optical parameters. Additionally, the method may include determining a plurality of final optical parameters based on the adjusted plurality of optical parameters when a convergence criterion of the loss-values fulfils a convergence criterion.