3D Spectacle Frame Centration Without Test Frames or CAD
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Solution Overview
Problem
Conventional methods for determining centration parameters for spectacle lenses require a test frame and CAD data from the frame manufacturer, limiting the selection of spectacle frames for customers and increasing computational power requirements.
Innovation Solution
A computer-implemented method that uses calibrated images from different directions of view to create a 3D point cloud of the spectacle frame, allowing for the calculation of centration parameters without a test frame or CAD data, using geometric determination and machine learning to relate eye position and frame geometry, reducing computational power and enabling selection from a wider range of frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods use test frames and CAD data from manufacturers, then measurement precision is improved, but device complexity and customer inconvenience increase
Solution Approach 1:
The patent uses digital images and point clouds as virtual copies of the spectacle frame instead of requiring physical test frames. The frame is captured through multiple calibrated camera views and reconstructed into a 3D point cloud model, which serves as a sufficient digital representation for measurement purposes, eliminating the need for physical test frame hardware
Solution Approach 2:
The patent extracts only the essential geometric information needed for centration measurements directly from the captured images and point cloud data, without requiring the full CAD data package from manufacturers. By extracting key geometric features (frame shape, size, position) from simplified image data rather than complete manufacturing CAD files, the system reduces complexity while maintaining measurement capability
2Measurement precision
If CAD data from frame manufacturer is required, then measurement precision is improved, but adaptability decreases
Solution Approach 1:
The patent creates a universal measurement system that can handle any spectacle frame type without requiring frame-specific CAD data. The multi-view image capture and point cloud reconstruction approach works universally across different frame styles, materials, and designs, allowing the same system to measure both simple and complex frame geometries using a single standardized process
Solution Approach 2:
By creating digital copies (point clouds) of frames directly from images rather than requiring manufacturer CAD files, the system can accommodate any frame that can be photographed, dramatically expanding the range of measurable frames to include those not available in digital CAD format
3Measurement precision
If multiple calibrated images from different directions are processed, then measurement precision is improved, but computational power requirements increase
Solution Approach 1:
The patent segments the frame measurement process into distinct stages: capturing multiple views, reconstructing point clouds for each view, and then integrating these point clouds to extract centration parameters. This segmentation allows computational work to be distributed and optimized at each stage rather than processing all image data simultaneously, reducing peak computational power requirements
Solution Approach 2:
The system creates intermediate point cloud copies from the multiple images, which are then processed to extract geometric features. These point cloud representations serve as simplified intermediates that require less computational power to process than the original high-resolution multi-view images, while preserving the necessary geometric information for accurate measurement
Data Source
AI summary
A computer-implemented method for determining centring is disclosed. At least two calibrated images of the head which are captured simultaneously from different viewing directions are provided, and geometric parameters which describe the position of the eyes are determined by geometric position determination. A three-dimensional data set describing geometric parameters of the frame front is provided; the geometric parameters of the frame front and the geometric parameters describing the position of the eyes are brought into relation to each other with a rigid transformation; and the centring parameters are calculated from the geometric parameters describing the frame front and those describing the position of the eyes.