Multi-Camera Calibration With Search Windows for 3D Imaging
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Solution Overview
Problem
Existing calibration methods for multiple imaging apparatuses capturing a common three-dimensional space face challenges in accurately calibrating parameters due to significant differences in camera positions and orientations, leading to difficulties in extracting unique feature points and high processing loads.
Innovation Solution
A calibration method that generates search windows using obtained parameters to extract feature points, performs feature point matching within these windows, and calibrates parameters based on matching results, thereby reducing processing load and increasing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If feature point extraction is performed on images from multiple imaging apparatuses with different positions and orientations, then calibration accuracy can be improved, but the processing load increases significantly
Solution Approach 1:
The patent divides the image into multiple search windows, where each window is a smaller region containing potential feature points. By segmenting the image this way, the system can process only the relevant portions containing feature points rather than the entire image, thereby reducing processing load while maintaining calibration accuracy through comprehensive feature point extraction across all windows.
Solution Approach 2:
The patent applies different processing strategies to different regions of the image by creating search windows centered on detected feature points. Each search window focuses on a local region with specific characteristics, allowing the system to extract feature points efficiently from areas most likely to contain useful calibration information while ignoring irrelevant regions.
2Measurement precision
If search windows are generated using obtained parameters to extract feature points, then feature point extraction accuracy improves, but device complexity increases
Solution Approach 1:
The patent performs preliminary actions by generating search windows before extracting feature points. The search windows are pre-calculated based on obtained parameters and image characteristics, creating a structured framework that guides the subsequent feature point extraction process. This preliminary organization simplifies the overall system by providing clear guidelines for where and how to search for feature points.
Solution Approach 2:
The search windows serve as an intermediary structure between the obtained parameters and the feature point extraction process. Rather than directly transforming parameters into feature point coordinates, the system uses search windows as an intermediate step that bridges these components, making the system more manageable and easier to implement while maintaining accuracy.
3Reliability
If feature point matching is performed between images, then calibration reliability improves, but processing time increases
Solution Approach 1:
The patent segments the feature point matching process by first extracting feature points within individual search windows before performing matching between images. This segmentation allows the system to work with a manageable set of candidate feature points from each window rather than all possible feature points in the entire image, reducing processing time while maintaining reliability through systematic evaluation of matches.
Solution Approach 2:
The patent extracts feature points from multiple search windows, potentially including more feature points than strictly necessary for calibration. This partial or excessive action ensures that sufficient feature points are available for reliable calibration while the search window structure provides a natural filter that prevents excessive processing by limiting the search space to relevant regions.
Data Source
AI summary
A calibration method of calibrating, using a processor, parameters of a plurality of imaging apparatuses that capture a common three-dimensional space, includes: obtaining images captured by the plurality of imaging apparatuses; obtaining the parameters of the plurality of imaging apparatuses; for each of the images, generating at least one search window to extract a plurality of feature points of the image using the parameters; for each of the images, extracting the plurality of feature points from an inside of the at least one search window; performing feature point matching between the images using the plurality of feature points; and calibrating the parameters based on a plurality of matching results obtained.