Single-shot structured light profilometry (SLP) aims at reconstructing the 3D height map of an object from a single deformed fringe pattern and has long been the ultimate goal in fringe projection profilometry. Recently, deep learning was introduced into SLP setups to replace the task-specific algorithm of fringe demodulation with a dedicated neural network. Research on deep learning-based profilometry has made considerable progress in a short amount of time due to the rapid development of general neural network strategies and to the transferrable nature of deep learning techniques to a wide array of application fields. The selection of the employed loss function has received very little to no attention in the recently reported deep learning-based SLP setups. In this paper, we demonstrate the significant impact of loss function selection on height map prediction accuracy, we evaluate the performance of a range of commonly used loss functions and we propose a new mixed gradient loss function that yields a higher 3D surface reconstruction accuracy than any previously used loss functions.

单次结构光轮廓测量 (SLP) 旨在从单个变形条纹图案重建物体的 3D 高度图，长期以来一直是条纹投影轮廓测量的最终目标。最近，深度学习被引入到 SLP 设置中，以用专用神经网络替换特定任务的条纹解调算法。由于通用神经网络策略的快速发展以及深度学习技术在广泛的应用领域的可迁移性，基于深度学习的轮廓测量法的研究在短时间内取得了长足的进步。在最近报道的基于深度学习的 SLP 设置中，所采用的损失函数的选择几乎没有受到关注。在本文中，