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LIU Yuhao, WU Fupei, WU Shuzhuang, WANG Rui. Binocular 3D reconstruction method based on interpolation super-resolution[J]. Chinese Optics. doi: 10.37188/CO.2023-0214
Citation: LIU Yuhao, WU Fupei, WU Shuzhuang, WANG Rui. Binocular 3D reconstruction method based on interpolation super-resolution[J]. Chinese Optics. doi: 10.37188/CO.2023-0214

Binocular 3D reconstruction method based on interpolation super-resolution

doi: 10.37188/CO.2023-0214
Funds:  Supported by National Natural Science Foundation of China (No. 61573233), National Natural Science Foundation of Guangdong, China (No. 2021A1515010661), Special Projects in Key Field of Colleges and Universities in Guangdong Province (No. 2020ZDZX2005)
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  • The reconstruction of the three-dimensional surface morphology of objects based on binocular stereo matching is constrained by physical conditions such as sensor size, lens focal length, and environmental light. A binocular surface three-dimensional reconstruction method based on interpolation super-resolution is proposed in response to this issue. First, at the image preprocessing stage, an image enhancement method based on wavelet transform and dual histogram equalization fusion is established to overcome the problems of traditional binocular vision limited by complex environmental light interference. Second, a super-resolution algorithm based on Lagrange and cubic polynomial interpolation is constructed to increase the image's pixel density and add image details to the binocular matching cost calculation stage, thereby improving the matching accuracy. Finally, a simple linear iterative clustering (SLIC) method is used to segment the target image, and a secondary surface fitting is performed for each segmented area to obtain a height curve that is more closely aligned with the actual surface of the object, which can reduce the reconstruction error and improve the reconstruction accuracy. The experimental results show that the average relative error of the global height measurement of 5 sets of measurement samples is ±2.3%, the average measurement time of the experiment is 1.8828 s, and the maximum time is 1.9362 s. This is a significant improvement over traditional methods. Experimental analysis results verify the effectiveness of the proposed algorithm.

     

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