Geometric Distillation from Rectified Stereo: Leveraging Epipolar Cues for Monocular Depth

Monocular depth foundation models have demonstrated re-markable generalization capabilities across diverse environments. How-ever, they continue to struggle with metric depth estimation in diverseenvironments. This limitation stems from the inherent scale ambiguity ofsingle-view inference, leading to misaligned scale predictions even whenthe relative geometry is accurate. Conversely, recent multi-view founda-tion models leverage cross-view cues to learn robust scene-level geometryand consistent scale. Yet, these benefits typically vanish during single-image inference, as the absence of explicit geometric constraints causesperformance to degrade. To bridge this gap, we propose a novel frame-work that transfers the scale-aware geometric priors of multi-view modelsinto monocular depth foundation models. Specifically, we introduce anEpipolar Distillation (EpiDistill), an approach utilizing Rectified StereoTokens, which enables the single-view prediction model to retain epipolarattention patterns and maintain geometric consistency without requiringmulti-view inputs at inference. Experimental results demonstrate thatour method significantly improves zero-shot metric depth estimation,particularly on challenging datasets like ETH3D and DIODE where scalealignment is critical. Furthermore, our approach is model-agnostic, con-sistently boosting the performance of state-of-the-art ViT-based models,including UniDepthV2 and DepthPro. Project Link