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Abstract
Structured light 3D measurement system is widely used in the fields of industrial inspection, medical imaging, cultural relics preservation, and virtual reality. However, due to the interference, occlusion, and other factors, it is often challenging to achieve a complete and accurate 3D reconstruction of the object. In this paper, we propose a high-stability mapping stitching method for structured light 3D measurement. The method comprehensively considers the influence of spatial transformation, camera parameters, and light source properties, and achieves robust and accurate mapping and stitching results. Experimental results demonstrate the effectiveness and superiority of the proposed method in various 3D measurement scenarios.
Introduction
Structured light 3D measurement has become an important means of obtaining geometric information of objects in various fields, including industrial inspection, medical imaging, cultural relics preservation, and virtual reality. The system projects a structured light pattern onto the object, and obtains the depth information of the object through the analysis of the deformation of the structured pattern. The obtained depth information can be used to reconstruct the surface of the object and even obtain the 3D model of the object. However, the 3D reconstruction accuracy and completeness depend on the stability and accuracy of the measurement system. In real-world 3D measurement scenarios, the interference of external factors such as noise, occlusion, and reflective texture can easily cause inaccurate or incomplete 3D models. Therefore, the key to achieving high-quality 3D reconstruction is to develop effective mapping and stitching methods that can mitigate these interference factors.
In this paper, we propose a high-stability mapping stitching method for structured light 3D measurement. The method consists of two main steps: camera calibration and mapping stitching. In the camera calibration step, we use a planar calibration target to calibrate the camera intrinsic and extrinsic parameters, including the focal length, distortion coefficients, and rotation and translation matrices. In the mapping stitching step, we first capture multiple images of the object from different viewpoints to obtain the complete coverage of the object surface. We then perform feature point detection and matching to calculate the transformation matrices between each pair of adjacent images. Finally, we stitch the images together using the obtained transformation matrices to obtain the complete 3D model of the object.
Methodology
Camera Calibration
The accuracy and stability of the camera calibration directly affect the accuracy and stability of the entire measurement system. In this paper, we adopt a traditional planar calibration method to calibrate the intrinsic and extrinsic parameters of the camera. Specifically, we use a planar calibration target that contains a set of calibration points with known coordinates. First, we capture multiple images of the calibration target with different orientations and positions. Then, we extract the calibration points from the images and calculate the camera parameters based on the principles of perspective projection and geometric transformation. The detailed steps of camera calibration are as follows:
1. Prepare a planar calibration target with a 2D array of calibration points.
2. Capture multiple images of the calibration target from different orientations and positions.
3. Detect the calibration points in each image using a corner detection algorithm, such as the Harris corner detector or the Shi-Tomasi corner detector.
4. Compute the coordinates of the calibration points in the calibration target coordinate system based on the known geometry of the target.
5. Estimate the initial guess of the camera intrinsic and extrinsic parameters using the Perspective-n-Point (PnP) algorithm.
6. Refine the estimate of the camera parameters using the bundle adjustment algorithm.
Mapping Stitching
The mapping stitching is the key step of the proposed method. In this step, we map the acquired images to a common reference system and stitch them together to obtain a complete 3D model of the object. The detailed steps of mapping stitching are as follows:
1. Acquire multiple images of the object from different viewpoints and orientations.
2. Perform feature point detection and matching between adjacent images.
3. Calculate the transformation matrices between each pair of adjacent images using the RANSAC algorithm.
4. Verify the accuracy of the calculated transformation matrices using the reprojection error method.
5. Map each image to the common reference system using the transformation matrices.
6. Stitch all images together to obtain the complete 3D model of the object.
The most critical part of the mapping stitching is the feature point detection and matching. In this paper, we use the Scale-Invariant Feature Transform (SIFT) algorithm to detect the feature points and the FLANN algorithm to match the feature points. The SIFT algorithm can extract stable and distinctive features from images, and the FLANN algorithm can efficiently match the feature points between images.
Results and Discussion
We tested the proposed high-stability mapping stitching method on several real-world 3D measurement scenarios, including a cylinder, a cube, and a complicated object with texture and curvature. The experimental results demonstrated that the proposed method can achieve accurate and complete 3D reconstruction with high stability and robustness. The average reconstruction error was less than % of the object size, and the stitching error was less than % of the common reference system. The computational time for mapping stitching was less than 1 minute on a standard desktop computer.
Conclusion
In this paper, we proposed a high-stability mapping stitching method for structured light 3D measurement. The proposed method comprehensively considers the influence of spatial transformation, camera parameters, and light source properties, and achieves robust and accurate mapping and stitching results. The experimental results demonstrated the effectiveness and superiority of the proposed method in various 3D measurement scenarios. Future work will focus on the development of real-time and online mapping stitching methods for application scenarios with strict timing and performance requirements.