Abstract

In most existing studies based on fringe projector profilometry (FPP), the whole scenario is reconstructed, or the ideal experimental settings are established to segment the object easily. However, in real industrial scenarios, automated object detection and segmentation are essential to perform object-level measurement. To address the problem, a dual-wavelet feature interaction network (DWFI-Net) is developed in this paper to perform object phase-valid region segmentation, where both the background and shadow are removed. In our work, the modulation and wrapped phase maps are considered as inputs innovatively. The modulation maps provide abundant structures and textures, while the wrapped phase maps complement and enhance shadows and edges. An adaptive wavelet feature interaction (AWFI) module is presented to learn and fuse the features, where discrete wavelet transformation (DWT) is applied to decompose the features. An edge-aware discrete cosine transformation (EDCT) module is developed as a decoder, where the discrete cosine transformation (DCT) is applied to interpret the fused features. Qualitative and quantitative experiments are performed to verify the superiority of our DWFI-Net and its effectiveness on object-level three-dimensional measurement based on FPP.

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