Polarization 3D reconstruction based on multi-exposure fusion

Polarization-based 3D reconstruction offers high precision and low cost, yet existing methods overlook noise suppression during image acquisition. We propose a pixel-wise multi-exposure fusion method that selects optimal responses within the linear camera curve to reduce photon noise and nonlinear distortions. A highlight correction strategy further mitigates specular-gradient errors. Experiments show that the method effectively suppresses Gaussian and systematic noise, enhances polarization accuracy, and yields more precise surface normals and clearer 3D reconstruction details. Specifically, the root mean square error (RMSE) of the reconstructed surface normals is reduced by approximately 14.3% relative to the traditional method, which quantitatively demonstrates that our reconstruction results are closer to the ground truth.