绝对相位
人工智能
计算机科学
结构光三维扫描仪
光学
计算机视觉
轮廓仪
相位恢复
结构光
物理
傅里叶变换
表面光洁度
材料科学
扫描仪
相位噪声
复合材料
量子力学
作者
Jia Qian,Shijie Feng,Yixuan Li,Tianyang Tao,Jing Han,Qian Chen,Chao Zuo
出处
期刊:Optics Letters
[The Optical Society]
日期:2020-03-20
卷期号:45 (7): 1842-1842
被引量:143
摘要
Recovering the high-resolution three-dimensional (3D) surface of an object from a single frame image has been the ultimate goal long pursued in fringe projection profilometry (FPP). The color fringe projection method is one of the technologies with the most potential towards such a goal due to its three-channel multiplexing properties. However, the associated color imbalance, crosstalk problems, and compromised coding strategy remain major obstacles to overcome. Inspired by recent successes of deep learning for FPP, we propose a single-shot absolute 3D shape measurement with deep-learning-based color FPP. Through “learning” on extensive data sets, the properly trained neural network can “predict” the high-resolution, motion-artifact-free, crosstalk-free absolute phase directly from one single color fringe image. Compared with the traditional approach, our method allows for more accurate phase retrieval and more robust phase unwrapping. Experimental results demonstrate that the proposed approach can provide high-accuracy single-frame absolute 3D shape measurement for complicated objects.
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