3D Camera Calibration via 2D Color Contrast and Point Cloud
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Solution Overview
Problem
Current 3D camera calibration methods using 2D information are imprecise due to inaccuracies at edge positions of the checkerboard pattern, leading to errors in transform mapping between coordinate systems, and require multiple position captures, slowing down the calibration process.
Innovation Solution
A method utilizing a 3D object and background with color contrast to capture images and point clouds, computing a missing score of color changes, and optimizing transform parameters to achieve quick calibration by ensuring the transform mapping between the 3D camera and 2D camera coordinate systems is accurate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the 3D information of the checkerboard pattern is used for calibration, then the calibration can be performed without color information, but the accuracy decreases at edge positions leading to transform mapping errors
Solution Approach 1:
The patent introduces color information as a new calibration target by displaying different colors (e.g., red, green, blue, yellow) at different positions of the calibration pattern. The 3D camera captures color information along with depth information, and the system calibrates by comparing the captured colors with expected colors at corresponding positions. This resolves the contradiction by providing an alternative calibration method that works well at edge positions where traditional 3D geometry-based calibration fails.
2Measurement precision
If the checkerboard pattern is moved to multiple positions for photographing, then more calibration data is obtained, but the calibration time increases
Solution Approach 1:
The patent transitions from traditional 2D image-based calibration to 3D color-aware calibration by utilizing the third dimension (depth) captured by the 3D camera. The calibration pattern is designed to be photographed from multiple angles simultaneously, and the system uses color information at each 3D position to establish accurate correspondences. This dimensional approach allows comprehensive calibration data collection in a single setup position, eliminating the need for time-consuming multi-position photography while maintaining high precision.
Data Source
AI summary
A method for calibrating a 3D camera includes arranging a 3D object and a background with contrast color on surfaces, and capturing the 3D object to get an image by a 2D camera and capturing a point cloud by the 3D camera in a one-pass operation. The raw colors of the 3D object and the background of the point cloud are separated and recorded. The point cloud of the 3D object and the background are transformed into the position of the corresponding pixel in the image to get transformed colors. The missing score is computed based on color changes of the point cloud. The transform parameter transforming the coordinate systems of the cameras is optimized to reduce the missing score, so as to quickly calibrate the 3D camera.