期刊
OPTICS AND LASERS IN ENGINEERING
卷 120, 期 -, 页码 21-30出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.optlaseng.2019.02.009
关键词
Structured-light sensing; Diffraction optical element; 3D reconstruction; Feature detection
类别
资金
- National Key R&D Program of China [2017YFB1103602]
- Shenzhen Key Laboratory Project [ZDSYS201707271637577]
- Shenzhen Science Plan [KQJSCX20170731165108047, JCYJ20170818160448602]
- National Natural Science Foundation of China [51705513, U1713213]
- NSFC-GuangDong [2017A030310474]
This paper presents a compact and accurate three-dimensional (3D) sensing system that employs a diffraction optical element as a projection device. Compared with the conventional laser speckle-based 3D sensing methods, a gridline pattern is utilized instead of a dot pattern. The proposed pattern is designed according to a pseudorandom coding scheme, and eight geometrical elements are embedded into the grid cells to form a unique codeword for each defined grid-point. By extracting the grid-points with the proposed feature detector, a topological graph is established to separate each pattern element. A convolutional neural network is trained for robust identification of the projected pattern elements. Finally, a codeword-correction procedure is applied to refine the decoding results. Using the proposed system-calibration method, accurate 3D reconstruction can be realized for the decoded grid-points. The measurement of the planarity and step distance has an absolute mean error of only 0.2-0.3 mm, indicating that it is far more accurate than the measurement using classical laser speckle-based 3D sensors. To demonstrate robustness of the proposed decoding algorithms, targets with plentiful color and texture are used. The results show that most of the grid-points can be robustly detected and that complex surfaces such as human faces and bodies can be precisely reconstructed.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据