Three-dimensional (3D) shape reconstruction from a monocular two-dimensional (2D) image has emerged as a highly demanded tool in many applications. This paper presents a novel 3D shape reconstruction technique that employs an end-to-end deep convolutional neural network (CNN) to transform a single speckle-pattern image into its corresponding 3D point cloud. In the proposed approach, three CNN models are explored for comparison to find the best capable network. To train the models with reliable datasets in the learning process, a multi-frequency fringe projection profilometry technique is adopted to prepare high-accuracy ground-truth 3D labels. Unlike the conventional 3D imaging and shape reconstruction techniques which often involve complicated algorithms and intensive computation, the proposed technique is simple, yet very fast and robust. A few experiments have been conducted to assess and validate the proposed approach, and its capability provides promising solutions to the ever-increasing scientific research and engineering applications.

从单眼二维（2D）图像进行三维（3D）形状重构已成为许多应用程序中急需的工具。本文提出了一种新颖的3D形状重构技术，该技术采用端到端的深度卷积神经网络（CNN）将单个散斑图样图像转换为其对应的3D点云。在提出的方法中，探索了三个CNN模型以进行比较，以找到功能最强大的网络。为了在学习过程中用可靠的数据集训练模型，采用了多频条纹投影轮廓测量技术来制备高精度的地面真实3D标签。与通常涉及复杂的算法和密集计算的常规3D成像和形状重建技术不同，所提出的技术很简单，但是非常快速且健壮。