A novel microscopic Shape-From-Polarization (SFP) 3D shape reconstruction method is proposed, in which degree of polarization is calculated using Polarized Bidirectional Reflectance Distribution Function (PBRDF) to overcome azimuthal ambiguity for both specular and diffuse polarization microscopic scenes. Based on the calculated degree of polarization, a polarization modulation microscopy oriented variational model is established to estimate surface normal vector and rebuild 3D shape of the sample with "fuzzy guidance" based on atomic force microscopy (AFM). Experiments on different microscopic samples indicate the proposed method can restore the stereoscopic morphology distribution with precision in micrometer.
This paper makes a first attempt to combines meta-transfer learning and generative adversarial networks (GAN) technology to solve the Shape from Polarization (SfP) problem. To solve this physics-based ill-posed problem, some researchers choose to blend these physical models as priors into a neural network architecture however cannot meet the requirements of cross-domain and few-shot polarization data. This proposed approach put forward two innovative points. First, we design the meta-transfer method so adapt GAN for few-shot learning tasks. Second, we introduce physical priors between monocular polarization sequence and 3D normal vector into the generative loss term. We report to exceed the previous state-of-the-art on deepsfp dataset, showing the potential of meta-transfer learning in few-shot generative tasks.
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