IEEE Trans Image Process. 2016 Apr;25(4):1726-39. doi: 10.1109/TIP.2016.2530313. Epub 2016 Feb 15.
In this paper, a robust fringe projection profilometry (FPP) algorithm using the sparse dictionary learning and sparse coding techniques is proposed. When reconstructing the 3D model of objects, traditional FPP systems often fail to perform if the captured fringe images have a complex scene, such as having multiple and occluded objects. It introduces great difficulty to the phase unwrapping process of an FPP system that can result in serious distortion in the final reconstructed 3D model. For the proposed algorithm, it encodes the period order information, which is essential to phase unwrapping, into some texture patterns and embeds them to the projected fringe patterns. When the encoded fringe image is captured, a modified morphological component analysis and a sparse classification procedure are performed to decode and identify the embedded period order information. It is then used to assist the phase unwrapping process to deal with the different artifacts in the fringe images. Experimental results show that the proposed algorithm can significantly improve the robustness of an FPP system. It performs equally well no matter the fringe images have a simple or complex scene, or are affected due to the ambient lighting of the working environment.
本文提出了一种基于稀疏字典学习和稀疏编码技术的鲁棒条纹投影轮廓术（FPP）算法。在重建物体的 3D 模型时，如果捕获的条纹图像具有复杂的场景，例如具有多个遮挡物体，传统的 FPP 系统通常无法执行。这给 FPP 系统的相位展开过程带来了很大的困难，可能会导致最终重建的 3D 模型严重失真。对于所提出的算法，它将相位展开所必需的周期顺序信息编码到一些纹理图案中，并将其嵌入到投影的条纹图案中。当捕获编码的条纹图像时，执行修改后的形态成分分析和稀疏分类过程，以解码和识别嵌入的周期顺序信息。然后，它用于辅助相位展开过程，以处理条纹图像中的不同伪影。实验结果表明，所提出的算法可以显著提高 FPP 系统的鲁棒性。无论条纹图像的场景是简单还是复杂，或者由于工作环境的环境照明而受到影响，它的性能都同样出色。
