SeeClear: Reliable Transparent Object Depth Estimation via Generative Opacification

Monocular depth estimation remains challenging for trans-parent objects, where refraction and transmission are difficult to modeland break the appearance assumptions used by depth networks. As a re-sult, state-of-the-art estimators often produce unstable or incorrect depthpredictions for transparent materials. We propose SeeClear, a novelframework that converts transparent objects into diffusion-generated opaqueimages, enabling stable monocular depth estimation for transparent ob-jects. Given an input image, we first localize transparent regions andtransform their refractive appearance into geometrically consistent opaqueappearances using a diffusion-based generative opacification module. Theprocessed image is then fed into an off-the-shelf monocular depth esti-mator without retraining or architectural changes. To train the opaci-fication model, we construct SeeClear-396k, a synthetic dataset con-taining 396k rendered images across 66k paired transparent-opaque con-figurations. Experiments on both synthetic and real-world datasets showthat SeeClear significantly improves depth estimation for transparentobjects.