论文标题
深度学习的空间阶段未包装3D测量
Deep Learning-enabled Spatial Phase Unwrapping for 3D Measurement
论文作者
论文摘要
就3D成像速度和系统成本而言，单摄像机系统投射单频模式是所有提议的边缘投影概要仪（FPP）系统中的理想选择。该系统需要强大的空间相解开（SPU）算法。但是，在复杂的场景中，强大的SPU仍然是一个挑战。质量引导的SPU算法需要更有效的方法来识别相位图中不可靠的点，然后再拆开。端到端深度学习SPU方法面临通用性和解释性问题。本文提出了一种混合方法，该方法结合了深度学习和FPP中强大的SPU的传统路径遵循。该混合动力SPU方案比传统的质量引导的SPU方法表现出更好的鲁棒性，比端到端深度学习方案更好的解释性以及对看不见的数据的一般性。在多个照明条件和多个FPP系统的实际数据集上进行的实验，图像分辨率不同，条纹的数量，边缘方向和光学波长验证了所提出方法的有效性。
In terms of 3D imaging speed and system cost, the single-camera system projecting single-frequency patterns is the ideal option among all proposed Fringe Projection Profilometry (FPP) systems. This system necessitates a robust spatial phase unwrapping (SPU) algorithm. However, robust SPU remains a challenge in complex scenes. Quality-guided SPU algorithms need more efficient ways to identify the unreliable points in phase maps before unwrapping. End-to-end deep learning SPU methods face generality and interpretability problems. This paper proposes a hybrid method combining deep learning and traditional path-following for robust SPU in FPP. This hybrid SPU scheme demonstrates better robustness than traditional quality-guided SPU methods, better interpretability than end-to-end deep learning scheme, and generality on unseen data. Experiments on the real dataset of multiple illumination conditions and multiple FPP systems differing in image resolution, the number of fringes, fringe direction, and optics wavelength verify the effectiveness of the proposed method.
