Monocular depth estimation is a fundamental computer vision task. Recovering
3D depth from a single image is geometrically ill-posed and requires scene
understanding, so it is not surprising that the rise of deep learning has led
to a breakthrough. The impressive progress of monocular depth estimators has
mirrored the growth in model capacity, from relatively modest CNNs to large
Transformer architectures. Still, monocular depth estimators tend to struggle
when presented with images with unfamiliar content and layout, since their
knowledge of the visual world is restricted by the data seen during training,
and challenged by zero-shot generalization to new domains. This motivates us to
explore whether the extensive priors captured in recent generative diffusion
models can enable better, more generalizable depth estimation. We introduce
Marigold, a method for affine-invariant monocular depth estimation that is
derived from Stable Diffusion and retains its rich prior knowledge. The
estimator can be fine-tuned in a couple of days on a single GPU using only
synthetic training data. It delivers state-of-the-art performance across a wide
range of datasets, including over 20% performance gains in specific cases.
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从单幅图像恢复 3D 深度是一项基础的计算机视觉任务，本研究提出了一种基于稳定扩散方法的仿射不变单目深度估计方法 Marigold，通过使用先前生成扩散模型中捕获的大量先验知识，该方法在各种数据集上取得了最先进的性能表现。