Reducing prior dependence in shape from polarization via ambiguous gradient analysis

Shape from polarization (SfP) inherently encounters surface normal azimuth ambiguity, which hinders accurate depth estimation. Traditional methods attempt to resolve the ambiguity by incorporating prior information through high-constraint pixel-wise normal correction, making their accuracy heavily dependent on the quality of the prior. In this paper, a low-constraint prior-guided robust polarization 3D imaging framework is proposed to address ambiguity by combining gradient analysis with geometric constraints. Unlike the traditional method, our method performs regional processing to achieve accurate normal estimation, which significantly decreases sensitivity to priors, thus allowing for application to objects with a specular-diffuse hybrid model. Several experiments were conducted on both synthetic and real datasets to evaluate the effectiveness of our method, and the results indicate remarkable performance, achieving an approximately 20-fold reduction in dependence on prior information compared to the traditional method.