GeoPolar: Few-Step RGB-to-Polarization Diffusion with Geometry Priors

Polarization reveals surface orientation and reflection properties that are useful for depth and shape estimation, but capturing it requires dedicated sensors, so the cue is absent from ordinary RGB images. Generative models have therefore been proposed to synthesize polarization from RGB. However, the angle of polarization depends not only on surface orientation but also on whether reflection is diffuse or specular, and the two cases yield angles 90° apart that appearance alone cannot distinguish. We resolve this diffuse–specular ambiguity by leveraging the geometric prior of monocular foundation models. From the same RGB image, such a model recovers the scene geometry, which we convert into the angular coordinates of polarization rather than supplying it as raw normals. The network then only needs to select between the two candidate angles instead of regressing the angle from scratch. Because the foundation-model prior carries the geometric burden of the task, the generator can remain lightweight: a four-step diffusion model operating directly in pixel space. GeoPolar is an order of magnitude lighter than latent-diffusion baselines and remains lighter with its geometry model, yet it surpasses them on benchmark data at native resolution and on most metrics of a set captured with an unseen camera. Further analyses show that the gain follows the geometric content of the prior and persists under moderate errors in the normals.