GeoDiff: Geometry-Guided Diffusion for Metric Depth Estimation

• URL: http://arxiv.org/abs/2510.18291v1
• Date: Tue, 21 Oct 2025 04:47:36 GMT
• Title: GeoDiff: Geometry-Guided Diffusion for Metric Depth Estimation
• Authors: Tuan Pham, Thanh-Tung Le, Xiaohui Xie, Stephan Mandt,
• Abstract summary: We introduce a novel framework for metric depth estimation that enhances pretrained diffusion-based monocular depth estimation (DB-MDE) models with stereo vision guidance.<n>Our training-free solution seamlessly integrates into existing DB-MDE frameworks and generalizes across indoor, outdoor, and complex environments.
• Score: 25.50613737995557
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a novel framework for metric depth estimation that enhances pretrained diffusion-based monocular depth estimation (DB-MDE) models with stereo vision guidance. While existing DB-MDE methods excel at predicting relative depth, estimating absolute metric depth remains challenging due to scale ambiguities in single-image scenarios. To address this, we reframe depth estimation as an inverse problem, leveraging pretrained latent diffusion models (LDMs) conditioned on RGB images, combined with stereo-based geometric constraints, to learn scale and shift for accurate depth recovery. Our training-free solution seamlessly integrates into existing DB-MDE frameworks and generalizes across indoor, outdoor, and complex environments. Extensive experiments demonstrate that our approach matches or surpasses state-of-the-art methods, particularly in challenging scenarios involving translucent and specular surfaces, all without requiring retraining.

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