Monocular gray code 3D shape measurement based on improved Siamese Network. Issue 1 (April 2021)

• Record Type: Journal Article
• Authors: Li, Yangming; Piao, Yan; Liu, Yuheng
• Is Part Of: Journal of physics. Volume 1848:Issue 1(2021)
• Publication Date: 2021-04
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1848/1/012162
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: As 3D information is widely used in more and more industries, simplification of 3D measurement operations and higher accuracy of measurement results are important development directions in the future. Compared with the binocular measurement system, simple equipment and convenient operation are the advantages of the monocular measurement; the disadvantage is that the measurement accuracy is low and it is greatly affected by the environment. This paper proposes a method that combines deep learning with traditional monocular Gray code 3D shape measurement technology. The space transformation network is used to improve the Siamese Network, and the single-view photos are taken through spatial transformation to obtain dual-view photos, and then matched through the Siamese Network. The purpose is to use monocular measurement equipment and methods to obtain the effect of binocular measurement. The deep learning network in this article has been pre-trained with a self-made data set before use. Compared with the original method, this method has higher accuracy in plane measurement, and has a significant improvement in depth measurement, successfully reducing the depth measurement error to less than 0.1mm.