Phase-shifting 3D profilometry is of high precision, but its performance could be far below expectations due to its long exposure time in the measurement of dynamic objects, especially human faces. In this article, a new idea is proposed to improve the measuring speed by providing reference derived from the statistical characteristics of human faces for the reconstruction of faces measured. During the measurement, no Gray code or low-frequency fringe is required, which reduces the number of patterns projected and sharply shortens the measuring time. Assisted by face detection and point cloud clustering, several candidate faces of different orders can be generated from the wrapped phase. Afterwards, the most probable candidate is selected according to the probability model of human face to obtain the correct result. Furthermore, the applicable conditions of our method have been analyzed, and the validity has been proved by experiments on actual human faces as well.

中文翻译：

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统计模型辅助的人脸高速相移 3D 轮廓测量


相移3D轮廓测量精度较高，但在测量动态物体尤其是人脸时，由于曝光时间较长，其性能可能远低于预期。本文提出了一种新的思路，通过人脸统计特征为测量人脸的重建提供参考，从而提高测量速度。测量过程中不需要格雷码或低频条纹，减少了投影图案的数量，大大缩短了测量时间。在人脸检测和点云聚类的辅助下，可以从包裹阶段生成多个不同阶的候选人脸。然后根据人脸的概率模型选择最可能的候选者以获得正确的结果。此外，还分析了该方法的适用条件，并通过实际人脸实验证明了该方法的有效性。