We propose SharpDepth, a novel approach to monocular metric depth estimation that combines the metric accuracy of discriminative depth estimation methods (e.g., Metric3D, UniDepth) with the fine-grained boundary sharpness typically achieved by generative methods (e.g., Marigold, Lotus). Traditional discriminative models trained on real-world data with sparse ground-truth depth can accurately predict metric depth but often produce over-smoothed or low-detail depth maps. Generative models, in contrast, are trained on synthetic data with dense ground truth, generating depth maps with sharp boundaries yet only providing relative depth with low accuracy. Our approach bridges these limitations by integrating metric accuracy with detailed boundary preservation, resulting in depth predictions that are both metrically precise and visually sharp. Our extensive zero-shot evaluations on standard depth estimation benchmarks confirm SharpDepth effectiveness, showing its ability to achieve both high depth accuracy and detailed representation, making it well-suited for applications requiring high-quality depth perception across diverse, real-world environments.

本文提出了SharpDepth，一种新颖的单目度量深度估计方法，旨在解决传统判别模型在生成精细深度图时过于平滑的问题。通过结合判别深度估计的度量准确性与生成方法的细致边界保留，SharpDepth在标准深度估计基准上进行了广泛的零-shot评估，显示出出色的深度准确性与视觉效果，有望在多样化的真实环境中提供高质量的深度感知。

SharpDepth：利用扩散蒸馏提升度量深度预测精度