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Fast Three-Dimensional Profilometry with Large Depth of Field

文献类型： 外文期刊

作者： Zhang, Wei 1 ; Zhu, Jiongguang 2 ; Han, Yu 1 ; Zhang, Manru 1 ; Li, Jiangbo 3 ;

作者机构： 1.Anhui Univ Finance & Econ, Dept Comp Technol & Sci, Bengbu 233030, Peoples R China

2.Foshan Polytech, Coll Intelligent Mfg, Foshan 528137, Peoples R China

3.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing 100097, Peoples R China

关键词： three-dimensional profilometry; large depth of field; time-domain Gaussian fitting; neural network

期刊名称：SENSORS （ 影响因子：3.4； 五年影响因子：3.7 ）

ISSN：

年卷期： 2024 年 24 卷 13 期

页码：

收录情况： SCI

摘要： By applying a high projection rate, the binary defocusing technique can dramatically increase 3D imaging speed. However, existing methods are sensitive to the varied defocusing degree, and have limited depth of field (DoF). To this end, a time-domain Gaussian fitting method is proposed in this paper. The concept of a time-domain Gaussian curve is firstly put forward, and the procedure of determining projector coordinates with a time-domain Gaussian curve is illustrated in detail. The neural network technique is applied to rapidly compute peak positions of time-domain Gaussian curves. Relying on the computing power of the neural network, the proposed method can reduce the computing time greatly. The binary defocusing technique can be combined with the neural network, and fast 3D profilometry with a large depth of field is achieved. Moreover, because the time-domain Gaussian curve is extracted from individual image pixel, it will not deform according to a complex surface, so the proposed method is also suitable for measuring a complex surface. It is demonstrated by the experiment results that our proposed method can extends the system DoF by five times, and both the data acquisition time and computing time can be reduced to less than 35 ms.

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