用立体视觉引导扩散模型，实现无需重训练的精准深度估计
GeoDiff: Geometry-Guided Diffusion for Metric Depth Estimation
- 将深度估计重构为逆问题，结合图像与立体几何约束
- 在复杂场景中优于当前最优方法，尤其对透明/反光表面表现优异
- 无需重新训练，可直接集成到现有单目深度模型中
我们提出一种新型框架，用于度量深度估计，通过引入立体视觉引导来增强预训练的基于扩散的单目深度估计（DB-MDE）模型。现有DB-MDE方法在预测相对深度方面表现优秀，但在单图场景下因尺度模糊性，难以准确估计绝对度量深度。为此，我们将深度估计重构为逆问题，利用预训练的潜在扩散模型（LDMs），在给定RGB图像条件下，结合基于立体的几何约束，学习尺度与偏移以实现精确深度恢复。该方法无需训练，可无缝集成至现有DB-MDE框架，并在室内、室外及复杂环境中具有良好泛化能力。大量实验表明，本方法在挑战性场景（如半透明和镜面表面）中达到或超越当前最先进水平，且无需重新训练。
原文摘要 · Abstract (English)
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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