In the fringe projection profilometry (FPP), traditionally, no clear mathematical expression was developed to design the sinusoidal fringe patterns for various objects. For this reason, we present an adaptive algorithm to generate the optimum fringe patterns with an oriented bounding box (OBB) and homography transform. Firstly, the features of various objects, which are segmented with deep learning network Mask R-CNN, are represented by the spindle orientation and length of the OBB. Secondly, the adaptive fringe patterns in the field of view of a camera are generated by the fusion with the OBB and the mathematical expression of conventional intensity fringe patterns. Finally, the fringe patterns in the field of view of a camera is transformed into the in the field of view of a projector by homography. Experiments have been carried out to validate the performances of the proposed method.
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