Deep-learning-assisted single-shot 3D shape and color measurement using color fringe projection profilometry

IF 1.1 4区 物理与天体物理 Q4 OPTICS
Kanami Ikeda, Takahiro Usuki, Yumi Kurita, Yuya Matsueda, Osanori Koyama, Makoto Yamada
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引用次数: 0

Abstract

The demand for fast, accurate, and cost-effective methods for three-dimensional shape and color measurements has been increasing. Ideally, both the shape and color of an object should be obtained in a single shot. Color fringe projection profilometry allows single-shot 3D shape measurement; however, it faces challenges when applied to colored objects. The fringe patterns are attenuated, leading to inaccuracies in shape measurement, and the fringes obscure the object's color information. This study proposes a novel approach to address these challenges by using a deep learning-based ResUNet model. Our method uses two independently trained ResUNets to correct fringe distortions for improved shape measurement accuracy and to remove fringe patterns for color information extraction from the same captured images. The simulation and experimental results demonstrate the effectiveness and applicability of this approach for single-shot 3D shape and color measurements.

使用彩色条纹投影轮廓术的深度学习辅助单镜头3D形状和颜色测量
对快速、准确和具有成本效益的三维形状和颜色测量方法的需求一直在增加。理想情况下,物体的形状和颜色都应该在一次拍摄中获得。彩色条纹投影轮廓术允许单镜头3D形状测量;然而,当应用于有色物体时,它面临着挑战。条纹图案被衰减,导致形状测量不准确,并且条纹模糊了物体的颜色信息。本研究提出了一种新的方法,通过使用基于深度学习的ResUNet模型来解决这些挑战。我们的方法使用两个独立训练的ResUNets来纠正条纹畸变以提高形状测量精度,并从相同的捕获图像中去除条纹图案以提取颜色信息。仿真和实验结果验证了该方法在单镜头三维形状和颜色测量中的有效性和适用性。
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来源期刊
Optical Review
Optical Review 物理-光学
CiteScore
2.30
自引率
0.00%
发文量
62
审稿时长
2 months
期刊介绍: Optical Review is an international journal published by the Optical Society of Japan. The scope of the journal is: General and physical optics; Quantum optics and spectroscopy; Information optics; Photonics and optoelectronics; Biomedical photonics and biological optics; Lasers; Nonlinear optics; Optical systems and technologies; Optical materials and manufacturing technologies; Vision; Infrared and short wavelength optics; Cross-disciplinary areas such as environmental, energy, food, agriculture and space technologies; Other optical methods and applications.
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