Three‐dimensional face modeling technology based on 5G virtual reality binocular stereo vision

Zhao, Hongjun; Wu, Bin

doi:10.1002/dac.4651

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…Stereo matching algorithms are crucial for understanding 3D scenes and reconstruction, and have been widely used in various fields, including robot navigation 1 , autonomous driving 2 , virtual reality 3 , and many others. These algorithms aim to calculate disparities, which represent the horizontal displacement of corresponding pixels in two rectified stereo pairs.…”

Section: Introductionmentioning

confidence: 99%

An application of stereo matching algorithm based on transfer learning on robots in multiple scenes

Bi,

Li,

Tong

et al. 2023

Sci Rep

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Robot vision technology based on binocular vision holds tremendous potential for development in various fields, including 3D scene reconstruction, target detection, and autonomous driving. However, current binocular vision methods used in robotics engineering have limitations such as high costs, complex algorithms, and low reliability of the generated disparity map in different scenes. To overcome these challenges, a cross-domain stereo matching algorithm for binocular vision based on transfer learning was proposed in this paper, named Cross-Domain Adaptation and Transfer Learning Network (Ct-Net), which has shown valuable results in multiple robot scenes. First, this paper introduces a General Feature Extractor to extract rich general feature information for domain adaptive stereo matching tasks. Then, a feature adapter is used to adapt the general features to the stereo matching network. Furthermore, a Domain Adaptive Cost Optimization Module is designed to optimize the matching cost. A disparity score prediction module was also embedded to adaptively adjust the search range of disparity and optimize the cost distribution. The overall framework was trained using a phased strategy, and ablation experiments were conducted to verify the effectiveness of the training strategy. Compared with the prototype PSMNet, on KITTI 2015 benchmark, the 3PE-fg of Ct-Net in all regions and non-occluded regions decreased by 19.3 and 21.1% respectively, meanwhile, on the Middlebury dataset, the proposed algorithm improves the sample error rate at least 28.4%, which is the Staircase sample. The quantitative and qualitative results obtained from Middlebury, Apollo, and other datasets demonstrate that Ct-Net significantly improves the cross-domain performance of stereo matching. Stereo matching experiments in real-world scenes have shown that it can effectively address visual tasks in multiple scenes.

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Section: Introductionmentioning

confidence: 99%

An application of stereo matching algorithm based on transfer learning on robots in multiple scenes

Bi,

Li,

Tong

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Stereo matching algorithms are crucial for understanding 3D scenes and reconstruction, and have been widely used in various fields, including robot navigation 1 , autonomous driving 2 , virtual reality 3 , and many others. The stereo matching algorithm calculates the disparity of the horizontal displacement of corresponding pixels in the two rectified stereo pairs.…”

Section: Introductionmentioning

confidence: 99%

An application of stereo matching algorithm based on transfer learning on robots in multiple scenarios

Wang

2023

Preprint

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Robot vision technology based on binocular vision has significant potential for development in various fields, including 3D scenes and reconstruction, target detection, autonomous driving, and other fields. To date, current binocular vision methods used in robotics engineering suffer from high costs, complex algorithms, and low reliability of the generated disparity map in multiple scenarios. Robots require a cost-effective algorithm with cross-domain generalization capabilities for multiple scenarios. To address these issues, a cross-domain stereo matching algorithm for binocular vision based on transfer learning was proposed in this paper, named Cross-Domain Adaptation and Transfer Learning Network (Ct-Net), which has shown valuable results in multiple robot scenes. First, this paper introduces a General Feature Extractor (GFE) to extract rich general feature information for domain adaptive stereo matching tasks. Then, a feature adapter is used to adapt the general features to the stereo matching network. Furthermore, a Domain Adaptive Cost Optimization Module (DACOM) was designed to optimize the matching cost. A disparity score prediction module was also embedded to adaptively adjust the search range of disparity and optimize the cost distribution. The overall framework was trained using a phased strategy, and ablation experiments were conducted to verify the effectiveness of the training strategy. On KITTI 2015 benchmark, compared with the prototype PSMNet, the 3PE − fg of Ct-Net in all regions and non-occluded regions decreased by 19.3% and 21.1% respectively. On the Middlebury dataset, the 2PE of Ct-Net achieved comparable results on all samples. The quantitative and qualitative results obtained from Middlebury, Apollo, and other datasets demonstrate that Ct-Net significantly improves the cross-domain performance of stereo matching. Stereo matching experiments in real-world scenarios have shown that it can effectively address visual tasks in multiple robot scenes.

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“…Binocular stereo vision simulates human eye vision to realize the mapping from two-dimensional (2D) images to threedimensional (3D) space and realizes the use of 3D information. At present, this method has been widely used in autonomous driving, robot navigation, virtual reality, and industrial production [1][2][3][4][5][6]. The process of solving this mapping relationship is called camera calibration, which involves some parameters, including both intrinsic and extrinsic parameters.…”

Section: Introductionmentioning

confidence: 99%

Camera Calibration for Long-Distance Photogrammetry Using Unmanned Aerial Vehicles

Zhang

Yang

et al. 2022

Journal of Sensors

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The traditional target-dependent camera calibration method has been widely used in close-distance and small field of view scenes. However, in view of the field coordinate measurement in the large-scale monitoring area under the complex field environment, the standard target can hardly meet the requirements of covering most of the camera’s field of view. In view of the above problem, a stereo camera calibration method is studied, using the unmanned aerial vehicles (UAV) as feature points, combined with the high-precision position information measured by the real-time kinematic (RTK) positioning system it carries. The measured UAV coordinates are unified in World Geodetic System 1984 (WGS-84). Therefore, through several preset points, the measurement reference coordinate system which is the new world coordinate system we need can be established in any monitoring area, which greatly improves the flexibility of measurement. The experimental results show that the measurement accuracy of the proposed method can reach 0.5% in the monitoring area with a diameter of 100 m. The calibration method has a wide range of application and does not need the traditional standard target, and the measurement reference coordinate system can be established according to the actual needs. It is suitable for field spatial coordinate measurement in long-distance and complex terrain environment.

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Three‐dimensional face modeling technology based on 5G virtual reality binocular stereo vision

Cited by 6 publications

References 18 publications

An application of stereo matching algorithm based on transfer learning on robots in multiple scenes

An application of stereo matching algorithm based on transfer learning on robots in multiple scenes

An application of stereo matching algorithm based on transfer learning on robots in multiple scenarios

Camera Calibration for Long-Distance Photogrammetry Using Unmanned Aerial Vehicles

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