Automatic identification and removal of outliers for high-speed fringe projection profilometry

Abstract

Phase-shifting profilometry combining with the two-frequency temporal phase unwrapping is widely used for high-speed, real-time acquisition of three-dimensional shapes. However, when the object is not motionless during the acquisition process, some unreliable results may emerge, especially around the contours of the measured object. The main reason for this is that the same point in the projected pattern sequence can map to different points within the camera images resulting from depth changes over time. We present a novel approach for identifying those invalid pixels affected by such an error. By carefully examining the captured fringe pattern, comparing two modulation maps, utilizing the phase relationship between two neighboring pixels, and employing a Gaussian filter to detect the protruding points, the bad measurement pixels can be detected and filtered out effectively. The whole procedure is of low computational complexity because of the introduced lookup table-based fast data processing method. Some experimental results are presented to verify the validity of our method.