结构光
结构光三维扫描仪
计算机科学
光学
轮廓仪
人工智能
投影(关系代数)
计量学
干扰(通信)
计算机视觉
材料科学
图像质量
深度学习
表面光洁度
物理
算法
图像(数学)
频道(广播)
扫描仪
复合材料
计算机网络
作者
Ruihu Zhang,Minghui Duan,Xin Fan,Yabing Zheng,Zheng Sun,Jinjin Zheng,Yi Jin
出处
期刊:Optics Express
[The Optical Society]
日期:2022-12-14
卷期号:30 (26): 47672-47672
被引量:2
摘要
Achieving high-quality surface profiles under strong ambient light is challenging in fringe projection profilometry (FPP) since ambient light inhibits functional illumination from exhibiting sinusoidal stripes with high quantization levels. Conventionally, large-step phase shifting approaches are presented to enhance the anti-interference capability of FPP, but the image acquisition process in these approaches is highly time-consuming. Inspired by the promising performance of deep learning in optical metrology, we propose a deep learning-enabled anti-ambient light (DLAL) approach that can help FPP extract phase distributions from a single fringe image exposed to unbalanced lighting. In this work, the interference imposed by ambient light on FPP is creatively modeled as ambient light-induced phase error (ALPE). Guided by the ALPE model, we generate the dataset by precisely adjusting the stripe contrast before performing active projection, overcoming the challenge of collecting a large sample of fringe images with various illumination conditions. Driven by the novel dataset, the generated deep learning model can effectively suppress outliers among surface profiles in the presence of strong ambient light, thereby implementing high-quality 3D surface imaging. Experimentally, we verify the effectiveness and adaptability of the proposed DLAL approach in both indoor and outdoor scenarios with strong irradiation.
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