斑点图案
计算机科学
亚像素渲染
人工智能
GSM演进的增强数据速率
计算机视觉
子网
匹配(统计)
特征(语言学)
模式识别(心理学)
像素
数学
计算机安全
语言学
统计
哲学
作者
Yanzhen Dong,Xiao Yang,Haitao Wu,Xiaobo Chen,Juntong Xi
出处
期刊:Measurement
[Elsevier]
日期:2023-03-01
卷期号:210: 112549-112549
被引量:3
标识
DOI:10.1016/j.measurement.2023.112549
摘要
Single-shot three-dimensional (3D) shape measurement method based on digital speckle correlation (DSC) has high measurement precision and only requires one pair of speckle images. Nevertheless, the matching of speckles is time-consuming and may lead to some data defects at large gradient variations and edge regions. This paper proposes an end-to-end speckle matching network to achieve fast, precise, and edge-preserved 3D measurement. Compared with other networks, the proposed network reduces the redundant channels in the Siamese feature-extraction subnetwork and constructs cost volume by group-wise correlation, making it possible to provide efficient representations at low computational consumption, thus being suitable for high-precision 3D measurement in large resolution. A lightweight 3D stacked hourglass subnetwork is used to aggregate cost information and furthermore, an edge refinement module is integrated by leveraging the left speckle image as a guide to regular edge details. A reliable speckle binocular dataset is prepared with a specialized device in a simple procedure and the labels are produced from the DSC method. Experiments demonstrate that the proposed network can produce dense disparity maps with subpixel precision and reduce single-shot inference time to 0.13 s with less GPU occupation, which is a significant improvement compared with the traditional DSC method and other learning-based methods. Moreover, the experiment validates that measurement precision of the reconstructed plane can reach less than 0.03 mm, meeting the requirements of most industrial applications.
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