Structured-Light Scanner Calibration via Transformation Mapping
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Solution Overview
Problem
Conventional calibration processes for structured-light scanners are time-consuming and costly, especially when high image quality or resolution is required, due to geometric distortions from optics and assembly errors.
Innovation Solution
A system and method that uses a structured-light scanner to capture base and calibration images, with a processor determining a transformation matrix to map the calibration image to the base image, transferring only the transformation matrix to the device under calibration, reducing the need for extensive data transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional calibration process is used for structured-light scanner, then calibration accuracy can be achieved, but calibration time and cost increase significantly
Solution Approach 1:
The patent extracts only the essential calibration information (transformation matrix parameters) from the complete calibration dataset, transferring only these critical parameters to the device under calibration. This extraction approach maintains calibration accuracy while dramatically reducing the time and computational resources required for calibration.
Solution Approach 2:
The calibration process is segmented into distinct stages: capturing base images without DUC, capturing calibration images with DUC, computing transformation mapping, and transferring only the essential transformation parameters to the DUC. This segmentation allows each stage to be optimized independently, reducing overall calibration time.
2Measurement precision
If conventional calibration process is used for structured-light scanner, then calibration accuracy can be achieved, but calibration cost increases due to extensive data processing
Solution Approach 1:
The patent extracts only the transformation matrix parameters from the complete calibration dataset, transferring only these essential parameters to the device under calibration. This extraction approach maintains calibration accuracy while dramatically reducing the computational resources, storage requirements, and associated costs.
Solution Approach 2:
Instead of transferring complete calibration datasets or ground truth images, the patent creates a simplified copy in the form of transformation matrix parameters that capture the essential calibration information. This parameter-based copying reduces data transfer costs and processing requirements while maintaining calibration effectiveness.
3Measurement precision
If high image quality or high resolution sensing is required, then image quality improves, but calibration time and cost increase substantially
Solution Approach 1:
The patent extracts only the transformation matrix parameters from the calibration process, which are sufficient to correct geometric distortions in high-resolution images. This extraction approach maintains the ability to calibrate high-quality images while avoiding the need to process and store large amounts of high-resolution calibration data, thus reducing calibration time.
4Measurement precision
If complete calibration data is transferred to DUC, then calibration accuracy is maintained, but data transfer time and processing load increase
Solution Approach 1:
The patent extracts only the transformation matrix parameters from the complete calibration dataset, identifying these as the minimal sufficient information needed for calibration. This extraction maintains calibration accuracy while reducing data transfer size and processing load, thereby improving calibration efficiency.
Solution Approach 2:
Instead of transferring complete calibration images and processing them at the DUC, the patent inverts the approach by computing the transformation mapping centrally and transferring only the resulting parameters to the DUC. This inversion of the calibration workflow significantly reduces data transfer requirements while maintaining calibration effectiveness.
Data Source
AI summary
A system for calibrating a three-dimensional scanning device includes a structured-light scanner capable of performing a structured-light operation, and a processor that performs calibration on a device under calibration (DUC). The structured-light scanner captures a base image by performing the structured-light operation prior to calibration. The structured-light scanner captures a calibration image with respect to corresponding DUC during calibration, and the calibration image is inputted to the processor, which determines transformation mapping from the calibration image to the base image. The determined transformation is then transferred to the DUC during calibration.