Zheng Yi, Wang Shaodong, Li Qing, Li Beiwen
Opt Express. 2020 Nov 23;28(24):36568-36583. doi: 10.1364/OE.410428.
High-accuracy and high-speed three-dimensional (3D) fringe projection profilometry (FPP) has been widely applied in many fields. Recently, researchers discovered that deep learning can significantly improve fringe analysis. However, deep learning requires numerous objects to be scanned for training data. In this paper, we propose to build the digital twin of an FPP system and perform virtual scanning using computer graphics, which can significantly save cost and labor. The proposed method extracts 3D geometry directly from a single-shot fringe image, and real-world experiments have demonstrated the success of the virtually trained model. Our virtual scanning method can automatically generate 7,200 fringe images and 800 corresponding 3D scenes within 1.5 hours.
高精度和高速三维（3D）条纹投影轮廓术（FPP）已在许多领域得到广泛应用。最近，研究人员发现深度学习可以显著改善条纹分析。然而，深度学习需要扫描大量物体以获取训练数据。在本文中，我们建议构建FPP系统的数字孪生，并使用计算机图形学进行虚拟扫描，这可以显著节省成本和劳动力。所提出的方法直接从单次拍摄的条纹图像中提取3D几何形状，实际实验证明了虚拟训练模型的成功。我们的虚拟扫描方法可以在1.5小时内自动生成7200幅条纹图像和800个相应的3D场景。
