Structure-Aware Consistency Priors for Shape from Polarization in Complex Media
Researchers have developed a novel method for recovering surface normals from single-view polarization images, specifically addressing the complexities of ice as a medium. Their approach, termed IceSfP, incorporates a structure-aware polarization prior based on autocorrelation functions to capture local spatial consistency. This prior is integrated into a dual-branch network that fuses raw polarization features with these priors, leading to more accurate surface normal estimation. The method reportedly outperforms existing techniques, achieving a mean absolute error of 16.01 degrees on a newly constructed real-world ice dataset. AI