Fringe projection techniques are widely used for precise three-dimensional depth profiling of objects. Existing signal processing-based fringe projection techniques measure the phase deformation of the projected fringe patterns with a sequence of operations, such as fringe denoising, fringe analysis for wrapped phase extraction, followed by phase unwrapping. However, the error induced in any of the stages leads to erroneous depth estimation. Furthermore, any aliasing in frequency domain fringe analysis and ill-posed nature of phase unwrapping limit the overall accuracy of the Fringe Projection Profilometry (FPP). To this end, unlike the traditional approaches, we propose a paradigm shift by introducing a novel end-to-end deep learning-based framework for FPP that does not need any frequency domain filtering and phase unwrapping. The proposed framework directly reconstructs the object’s depth profile from the deformed fringe itself through a multi-resolution similarity assessment convolutional neural network. We compare the performance of the proposed framework with two widely used conventional approaches. The evaluations are performed for various challenging and important scenarios such as low fringe-frequency, depth profiles with high dynamic range and noisy fringes. The results demonstrate that the proposed framework achieves promising results in all these scenarios.

条纹投影技术广泛用于对象的精确三维深度剖析。现有的基于信号处理的条纹投影技术通过一系列操作来测量投影条纹图案的相位变形，例如条纹降噪，用于包裹相位提取的条纹分析，然后进行相位展开。然而，在任何阶段中引起的误差都会导致错误的深度估计。此外，频域条纹分析中的任何混叠和相位解缠的不适性质限制了条纹投影轮廓仪（FPP）的整体精度。为此，与传统方法不同，我们通过引入新颖的端到端基于深度学习的FPP框架提出了范式转换，该框架不需要任何频域滤波和相位展开。所提出的框架通过多分辨率相似性评估卷积神经网络从变形条纹本身直接重建对象的深度轮廓。我们将提议的框架的性能与两种广泛使用的常规方法进行比较。针对各种挑战性和重要场景进行评估，例如低边缘频率，具有高动态范围的深度轮廓和高噪声边缘。结果表明，提出的框架在所有这些情况下均取得了可喜的成果。针对各种挑战性和重要场景进行评估，例如低边缘频率，具有高动态范围的深度轮廓和高噪声边缘。结果表明，提出的框架在所有这些情况下均取得了可喜的成果。针对各种挑战性和重要场景进行评估，例如低边缘频率，具有高动态范围的深度轮廓和高噪声边缘。结果表明，提出的框架在所有这些情况下均取得了可喜的成果。