A novel stereo image self-inpainting network for autonomous robots

Yang, Xiaokang; Li, Hengyu; Liu, Jingyi; Xie, Yonghao; Pu, Huayan; Xie, Shaorong; Luo, Jun

doi:10.1016/j.robot.2022.104197

Cited by 2 publications

(3 citation statements)

References 17 publications

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“…All regions (%) Noc regions (%) 3PE-bg 3PE-fg 3PE-all 3PE-bg 3PE-fg 3PE-all SGM [20] 5.06 Comparing experimental results of our method with other methods on the KITTI 2015 benchmark. "Noc" means non-occluded area; "fg" represents the foreground area; "bg" represents the background area; "all" represents the all area.…”

Section: Methodsmentioning

confidence: 99%

“…Lajoie et al 19 presented the Swarm-SLAM system for collaborative simultaneous localization and mapping, which can be effectively applied to swarm robotics. Yang et al 20 proposed a CNN-based binocular vision self-inpainting network for real-time stereo image inpainting of autonomous robots, achieving state-of-the-art performance on image inpainting. Shim et al 21 proposed an inspection robot and management system that utilizes stereo vision to inspect damage on concrete surfaces.…”

Section: Related Workmentioning

confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

Wang

2023

Preprint

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show abstract

Section: Related Workmentioning

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.

show abstract

A novel stereo image self-inpainting network for autonomous robots

Cited by 2 publications

References 17 publications

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

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

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

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