MetricGold: Leveraging Text-To-Image Latent Diffusion Models for Metric Depth Estimation

TL;DR

MetricGold introduces a novel method leveraging text-to-image latent diffusion models and synthetic data to improve zero-shot metric depth estimation from single images, achieving robust and high-quality results.

Contribution

The paper presents MetricGold, a new approach that combines latent diffusion, synthetic data, and log-scaled depth representation for improved metric depth estimation.

Findings

Robust generalization across diverse datasets.

Sharper and higher quality depth estimates.

Efficient training on a single GPU within two days.

Abstract

Recovering metric depth from a single image remains a fundamental challenge in computer vision, requiring both scene understanding and accurate scaling. While deep learning has advanced monocular depth estimation, current models often struggle with unfamiliar scenes and layouts, particularly in zero-shot scenarios and when predicting scale-ergodic metric depth. We present MetricGold, a novel approach that harnesses generative diffusion model's rich priors to improve metric depth estimation. Building upon recent advances in MariGold, DDVM and Depth Anything V2 respectively, our method combines latent diffusion, log-scaled metric depth representation, and synthetic data training. MetricGold achieves efficient training on a single RTX 3090 within two days using photo-realistic synthetic data from HyperSIM, VirtualKitti, and TartanAir. Our experiments demonstrate robust generalization across diverse datasets, producing sharper and higher quality metric depth estimates compared to existing approaches.