中国光学（中英文）2024，Vol.17Issue(4)：862-874,13.DOI:10.37188/CO.2023-0214
基于插值超分辨的双目三维重建方法
Binocular 3D reconstruction method based on interpolation super-resolution
摘要
Abstract
The reconstruction of the three-dimensional surface morphology of objects based on binocular ste-reo 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-resolu-tion 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 im-prove 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 experi-ment 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.关键词
双目视觉/三维测量/插值超分辨/超像素分割Key words
binocular vision/3D measurement/interpolation super-resolution/superpixel segmentation分类
信息技术与安全科学引用本文复制引用
刘宇豪,吴福培,吴树壮,王瑞..基于插值超分辨的双目三维重建方法[J].中国光学（中英文）,2024,17(4):862-874,13.基金项目
国家自然科学基金(No.61573233) （No.61573233）
广东省自然科学基金(No.2021A1515010661) （No.2021A1515010661）
广东省普通高校创新团队资助项目(No.2020KCXTD012)Supported by National Natural Science Foundation of China(No.61573233) （No.2020KCXTD012）
National Natural Science Found-ation of Guangdong,China(No.2021A1515010661) （No.2021A1515010661）
the Guangdong Provincial University Innovation Team Project(No.2020KCXTD012) （No.2020KCXTD012）
