人工智能
计算机科学
结构光
结构光三维扫描仪
计算机视觉
投影(关系代数)
转化(遗传学)
一次性
深度学习
弹丸
基本事实
轮廓仪
人工神经网络
目标捕获
光学
物理
算法
有机化学
扫描仪
生物化学
化学
量子力学
机械工程
工程类
表面粗糙度
基因
作者
Hieu Nguyen,Khanh L. Ly,Charlotte Qiong Li,Zhaoyang Wang
出处
期刊:Applied Optics
[The Optical Society]
日期:2022-10-03
卷期号:61 (29): 8589-8589
被引量:9
摘要
Learning three-dimensional (3D) shape representation of an object from a single-shot image has been a prevailing topic in computer vision and deep learning over the past few years. Despite extensive adoption in dynamic applications, the measurement accuracy of the 3D shape acquisition from a single-shot image is still unsatisfactory due to a wide range of challenges. We present an accurate 3D shape acquisition method from a single-shot two-dimensional (2D) image using the integration of a structured-light technique and a deep learning approach. Instead of a direct 2D-to-3D transformation, a pattern-to-pattern network is trained to convert a single-color structured-light image to multiple dual-frequency phase-shifted fringe patterns for succeeding 3D shape reconstructions. Fringe projection profilometry, a prominent structured-light technique, is employed to produce high-quality ground-truth labels for training the network and to accomplish the 3D shape reconstruction after predicting the fringe patterns. A series of experiments has been conducted to demonstrate the practicality and potential of the proposed technique for scientific research and industrial applications.
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