Mr. Fengyang Jiang Profile

Fengyang Jiang

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Proceedings Article | 5 December 2024 Paper

A deep learning-based stereo speckle image matching method for rapid 3D reconstruction of weakly-textured surfaces

Yahong Feng, Shijin Zhang, Yuqiang Wu, Fengyang Jiang

Proceedings Volume 13418, 134181S (2024) https://doi.org/10.1117/12.3048497

KEYWORDS: Speckle, 3D modeling, Cameras, Image processing, Connectors, Feature extraction, 3D metrology, Education and training, Convolution, 3D image reconstruction

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Currently, the 3D model reconstruction technology based on binocular stereo vision becomes very popular, however, the current stereo matching method is difficult to be applied for objects with weakly-textured surface, further leads to low accuracy and poor efficiency of 3D reconstruction for those objects. To improve the reconstruction accuracy and efficiency, a series of methods, such as image enhancement, better feature extraction algorithm and structured light technology have been searched. However, those methods provided are either costly or computationally complex, which lead to very limited application. To solve the above problem, a new method has been proposed in this paper, in which, using speckle patterns to enhance texture features and further to improve the 3D reconstruction accuracy of the weakly-textured surface. In addition, based on the analysis of the traditional speckle image matching methods, this new method takes advantage of the great potential of deep learning techniques to improve the matching accuracy and efficiency. Experiments demonstrate that the deep learning-based stereo speckle matching network achieves a matching accuracy of 10^-3pixels and efficiency of 9.37×10⁵ POI/S. With this new method, a fast and accurate 3D reconstruction of the weakly-textured surface can be achieved.

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