3D Image Generation System Motion Artifact Compensation
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Solution Overview
Problem
Intraoral scanners generate deformed three-dimensional images due to rapid movement and hand tremors, leading to poor image quality when capturing multiple images in the limited oral cavity.
Innovation Solution
A method and system that project light patterns onto an object to capture two-dimensional images, determine if they meet predetermined rules, select suitable images based on image change amounts, and generate point clouds for splicing to create high-quality three-dimensional images, using a processor and camera system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple two-dimensional images are captured rapidly to improve scanning efficiency, then productivity increases, but image quality deteriorates due to motion artifacts and hand tremors
Solution Approach 1:
The system performs preliminary evaluation of each captured two-dimensional image against predetermined rules (sharpness, noise level, motion artifacts) before incorporating it into the three-dimensional reconstruction. This preliminary filtering action ensures that only high-quality images are used, preventing motion artifacts from degrading the final image quality while maintaining rapid scanning throughput
Solution Approach 2:
The system implements feedback mechanisms where each captured image is evaluated and compared with previous images to detect motion artifacts and hand tremors. Based on this feedback, the system can adjust scanning parameters or selectively exclude degraded images from the reconstruction process, thereby maintaining image quality during rapid scanning
2Loss of information
If all captured two-dimensional images are used for three-dimensional reconstruction to improve completeness, then data coverage increases, but image quality deteriorates due to accumulation of errors from motion artifacts
Solution Approach 1:
The system applies different quality standards and processing treatments to different regions or individual images based on their specific characteristics. Images with motion artifacts or hand tremors are identified and either corrected locally or excluded from specific regions, while high-quality images are used for their complete coverage. This allows the system to maintain comprehensive data coverage while preventing error accumulation in the final three-dimensional reconstruction
3Loss of time
If scanning speed is increased to reduce examination time, then productivity improves, but measurement precision deteriorates due to hand tremors and motion blur
Solution Approach 1:
The system performs preliminary quality assessment of each captured image against predetermined criteria (sharpness, noise level, motion detection) before using it for reconstruction. This preliminary filtering ensures that even at high scanning speeds, only images meeting quality thresholds are incorporated, maintaining measurement precision while enabling rapid examination
Solution Approach 2:
Real-time feedback mechanisms monitor image quality during rapid scanning, detecting motion blur and hand tremor effects. The system uses this feedback to adjust scanning parameters or selectively process images, ensuring that measurement precision is maintained despite increased scanning speed and reduced examination time
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 quality of generated three-dimensional images by reducing noise and deformations, ensuring accurate representation of the oral cavity by selecting appropriate images and compensating for motion artifacts.
Implementation Method 1
projecting a plurality of light patterns onto an object to generate a plurality of groups of two-dimensional images
Data Source
AI summary
A three-dimensional image generation method includes projecting a plurality of light patterns to an object to generate a plurality of groups of two-dimensional images, determining if the plurality of groups of two-dimensional images meet a predetermined rule, selecting j groups of two-dimensional images from the plurality of groups of two-dimensional images according to an image change amount of each group of two-dimensional image of the plurality of groups of two-dimensional images if the plurality of groups of two-dimensional images meet a predetermined rule, using the j groups of two-dimensional images to form j point clouds, and using the j point clouds to perform a splicing operation to generate a three-dimensional image. Determining if the plurality of groups of two-dimensional images meet the predetermined rule includes at least determining if the plurality of groups of two-dimensional images are corresponding to a same area.