Beyond RGB: Polarization-Guided Surface-Aware 3D Generation

Single-view 3D generation relies on strong RGB generative priors. However, 3D inference from RGB appearance alone remains geometrically ambiguous: a model may produce a visually plausible object while failing to recover local surface orientation and fine-scale geometry, particularly in weakly textured or highly reflective regions. Unlike RGB appearance, the angle of linear polarization (AoLP) is related to surface orientation and provides a complementary cue to local geometry. We propose polarization-guided surface-aware 3D generation, which retains the prior of an RGB-conditioned generator while using polarization to refine its modeling of local surfaces. A surface-aware feature fusion module jointly encodes semantic and appearance information from RGB and orientation cues from AoLP, yielding a normal-aware condition for the denoising process of a pretrained 3D generator. To support training and evaluation, we also construct OBJMV3D with 60,000 paired groups of RGB images, AoLP observations, surface normals, and high-quality 3D assets. Experiments on this benchmark show that polarization-derived surface cues improve fine-scale geometry while preserving overall generation quality, with more pronounced geometric benefits where RGB evidence is limited by reflectance or weak texture.