Refinement of Inverse Depth Plane in Textureless and Occluded Regions in a Multiview Stereo Matching Scheme

Lee, Min-jae; Park, Soon-Yong

doi:10.1155/2022/7181445

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…To address the issues of gradient vanishing or exploding in convolutional neural networks, we utilize the GRU as the principal component of the network. In the context of stereo matching, a larger receptive field can effectively solve challenges arising from occlusion and noise, leading to more consistent matching outcomes [46]. However, this approach inevitably entails a significant loss of fine-grained detail.…”

Section: Cascaded Grumentioning

confidence: 99%

A cascaded GRU-based stereoscopic matching network for precise plank measurement

Xiong,

Li,

Liu

et al. 2024

Meas. Sci. Technol.

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Wooden plank images in industrial measurements often contain numerous textureless areas. Furthermore, due to the thin plate structure, the three-dimensional disparity of these planks is predominantly confined to a narrow range. Consequently, achieving accurate three-dimensional matching of wooden plank images has consistently presented a challenging task within the industry. In recent years, deep learning has progressively supplanted traditional stereo matching methods due to its inherent advantages, including rapid inference and end-to-end processing. Nonetheless, the acquisition of datasets for stereo matching networks poses an additional challenge, primarily attributable to the difficulty in obtaining accurate disparity data. Thus, this paper presents a novel stereo matching method incorporating three key innovations. Firstly, an enhanced Gated Recurrent Unit(GRU) network is introduced, accompanied by a redesigned structure to achieve higher matching accuracy. Secondly, an efficient preprocessing module is proposed, aimed at improving the algorithm's efficiency. Lastly, in response to the challenges posed by datasets acquisition, we innovatively employed image simulation software to obtain a high-quality simulated dataset of wooden planks. To assess the feasibility of our approach, we conducted both simulated and real experiments. The experiments results clearly exhibit the superiority of our method when compared to existing approaches in terms of both stability and accuracy. In the simulation experiment, our method attained a bad1.0 score of 2.1% (compared to the baseline method's 9.76%); In the real experiment, our method achieved an average error of 0.104mm (compared to the baseline method's 0.268mm). It is worth noting that our study aims to address the challenge of acquiring datasets for deep learning and bridging the gap between simulated and real data, resulting in increased applicability of deep learning in more industrial measurement domains.

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Section: Cascaded Grumentioning

confidence: 99%

A cascaded GRU-based stereoscopic matching network for precise plank measurement

Xiong,

Li,

Liu

et al. 2024

Meas. Sci. Technol.

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“…e camera system is a multicamera stereo vision system [17][18][19][20][21][22][23][24][25][26][27][28][29] composed of 4 infrared cameras, which can measure the object's coordinates in 3D space. Calibration software and calibration devices are provided by the manufacturer.…”

Section: Establishment Of the Camera Coordinate System (C)mentioning

confidence: 99%

Automatic Cutting System Design of Robot Hand Based on Stereo Vision

Mei¹,

Zou³

2022

Scientific Programming

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At present, the pouring riser of valve castings is mainly removed by manual cutting, polluting the environment and causing harm to the human body with a low efficiency. Therefore, an automatic cutting pouring riser method using the stereo vision system and manipulator is proposed. The relative position of the valve casting and the end of the manipulator is obtained through the position transformation of the valve casting coordinate system, the manipulator end of the coordinate system, and the camera coordinate system. The spatial motion trajectory of the manipulator is planned, implementing automatic pouring riser cutting of the same valve casting with the same pose. The experimental results show that the position deviation and the angle deviation of the repetitive positioning accuracy and the random positioning accuracy of the visual system are within ±1 mm and ±1°; in the pouring riser cutting test, the maximum deviation between the actual cutting trajectory and the theoretical cutting trajectory is 3 mm. In summary, the method shows a good reliability and could meet the requirements of cutting accuracy.

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