Online fringe projection profilometry based on scale-invariant feature transform

Abstract

An online fringe projection profilometry (OFPP) based on scale-invariant feature transform (SIFT) is proposed. Both rotary and linear models are discussed. First, the captured images are enhanced by “retinex” theory for better contrast and an improved reprojection technique is carried out to rectify pixel size while keeping the right aspect ratio. Then the SIFT algorithm with random sample consensus algorithm is used to match feature points between frames. In this process, quick response code is innovatively adopted as a feature pattern as well as object modulation. The characteristic parameters, which include rotation angle in rotary OFPP and rectilinear displacement in linear OFPP, are calculated by a vector-based solution. Moreover, a statistical filter is applied to obtain more accurate values. The equivalent aligned fringe patterns are then extracted from each frame. The equal step algorithm, advanced iterative algorithm, and principal component analysis are eligible for phase retrieval according to whether the object moving direction accords with the fringe direction or not. The three-dimensional profile of the moving object can finally be reconstructed. Numerical simulations and experimental results verified the validity and feasibility of the proposed method.