Optical cryptanalysis is essential to the further investigation of more secure optical cryptosystems. Learning-based attack of optical encryption eliminates the need for the retrieval of random phase keys of optical encryption systems but it is limited for practical applications since it requires a large set of plaintext-ciphertext pairs for the cryptosystem to be attacked. Here, we propose a two-step deep learning strategy for ciphertext-only attack (COA) on the classical double random phase encryption (DRPE). Specifically, we construct a virtual DRPE system to gather the training data. Besides, we divide the inverse problem in COA into two more specific inverse problems and employ two deep neural networks (DNNs) to respectively learn the removal of speckle noise in the autocorrelation domain and the de-correlation operation to retrieve the plaintext image. With these two trained DNNs at hand, we show that the plaintext can be predicted in real-time from an unknown ciphertext alone. The proposed learning-based COA method dispenses with not only the retrieval of random phase keys but also the invasive data acquisition of plaintext-ciphertext pairs in the DPRE system. Numerical simulations and optical experiments demonstrate the feasibility and effectiveness of the proposed learning-based COA method.

条纹投影轮廓术能较好地兼顾系统灵活性与测量精度，是光学三维表面成像与测量的主流技术。利用条纹投影轮廓术进行三维成像，首先需要建立合适的系统模型，然后通过系统标定来确定描述模型的系统参数，最后利用标定的系统模型进行三维重建，获得物体的三维表面形貌。由此可见，系统标定与系统模型密不可分，对三维成像的性能有直接影响。根据相位-三维映射和双目立体视觉两类不同的工作原理，对条纹投影轮廓术的系统模型和系统标定方法进行了综述，并简要总结了评估系统精度的方法和依据。

针对大尺寸复杂物体的全自动、高精度、大数据密度、真彩色三维成像与测量，基于条纹结构光三维传感器，阐述了多节点三维传感器测量网络相关技术。涉及单三维传感器的条纹分析和相位重建、系统标定和三维重建两大关键技术点分析，多节点三维传感器测量网络的构建与优化、多节点三维传感器测量网络的标定、测量三维深度数据与纹理数据的匹配与融合等相关技术。并给出了部分实验原型机及实验结果。