KEYWORDS: Speckle, RGB color model, Speckle pattern, 3D projection, Cameras, Reconstruction algorithms, Light sources and illumination, Education and training, 3D metrology, Projection systems
Stereo vision, as a critical part in the field of three-dimensional reconstruction, perceives and measures the 3D shape of objects by matching corresponding points between the left camera and the right camera views. The extremely challenging in stereo vision is to ensure the accuracy of matching areas and to obtain disparity robustly. However, passive stereo imaging has inherent problems for ill-posed areas such as complicated illumination, weak texture, and perspective occlusion in adverse environments such as industrial scenes. The active stereo technique designs and projects patterns to improve the accuracy of digital image correlation (DIC). Focusing on the intricate maintenance scenes of high-speed railways,we propose an active stereo strategy with RGB speckle projection patterns. Compared with previous speckle patterns, our RGB speckle pattern enhances more comprehensive texture information and demonstrates significant adaptability in diverse scenes. Comparative experiments were implemented on different stereo matching algorithms and projection strategies in industrial scenes,and the results demonstrate that the proposed approach achieves favorable reconstruction results and higher accuracy.
Gray code assisted phase shifting technology can achieve robust and noise-tolerated three-dimensional (3D) shape measurements. To solve the issues of unsynchronized brightness changes, local overexposure, and edge coding errors caused by inconsistent reflectivity of the surface in complex industrial scenes, as well as defocusing caused by noncontinuous surfaces and varying distances, we combine the advantages of a large imaging range in passive stereo vision and high precision in active structured light imaging. It uses a consumer-grade projector to project gray code and stripe patterns, whereas two precalibrated color industrial cameras capture raw images and obtain the original channel data. Gray code and reverse gray code images are projected to solve the problems of binarization and boundary blur. In addition, an error point filtering strategy is proposed to retain pixels with decoding errors of less than two bits. The use of softargmin for subpixel matching of absolute phase results in a high precision disparity map. We present a simple and high precision 3D measurement system for industrial objects. Experiments on 3D measurements in complex industrial scenes showed that the proposed method can achieve high precision and robust 3D shape measurements.
3D convolution based stereo matching network has a wide range of research prospects at present, such as 3D measurement, unmanned driving, etc., but there is still room for improvement in accuracy. This paper proposes a threedimensional matching method based on deep learning: In the feature extraction part, a multi-layer learning parameter guiding feature fusion module is proposed, which can preserve the pixel gradient of the edge when sampling under single channel image guide. Then, the instance whitening noise of the output feature map is calculated, which effectively eliminates image pixel shift and feature similarity through the covariance threshold. In addition to using the traditional SmoothL1 loss function, the algorithm calculates the stereo focus loss by designing the confidence detection network to adjust the cost volume. The algorithm is tested on SceneFlow and Kitti series datasets. Using a multi-layer guiding module, instance bleaching loss, and stereo focus loss simultaneously compared to the original version, the error between test result and Ground Truth in the first frame (D1 Loss) of are reduced by 30.6%(Kitti2015), and the three-pixel error (3PE) is reduced by 6.3%(Kitti2012), which verifies the effectiveness of the algorithm.
The use of spatial coding schemes is always a research hotspot of structural light 3D reconstruction. Spatial coding only needs one frame of image to reshape the three-dimensional feature of the object. However, it is difficult to obtain higher resolution due to fewer feature points extracted. In the coding stage, this paper uses a two-dimensional discrete pseudorandom pattern composed of rectangular color elements. And in the decoding stage, a feature detector for a rectangular grid point and a center point is proposed by using four corner points and a center point of a rectangle as feature points. It can get more feature points in the spatial coding without increasing the calculation amount during the decoding stage, thereby obtaining more accurate feature information on the surface of the object. From the experimental results, this method compared with the existing approaches can significantly improve the accuracy of rectangular grid points detection and can reconstruct more high-precision feature points.
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