Monocular depth estimation is crucial for numerous downstream vision tasks
and applications. Current discriminative approaches to this problem are limited
due to blurry artifacts, while state-of-the-art generative methods suffer from
slow sampling due to their SDE nature. Rather than starting from noise, we seek
a direct mapping from input image to depth map. We observe that this can be
effectively framed using flow matching, since its straight trajectories through
solution space offer efficiency and high quality. Our study demonstrates that a
pre-trained image diffusion model can serve as an adequate prior for a flow
matching depth model, allowing efficient training on only synthetic data to
generalize to real images. We find that an auxiliary surface normals loss
further improves the depth estimates. Due to the generative nature of our
approach, our model reliably predicts the confidence of its depth estimates. On
standard benchmarks of complex natural scenes, our lightweight approach
exhibits state-of-the-art performance at favorable low computational cost
despite only being trained on little synthetic data.

使用流场匹配的方法，通过引入预训练的图像扩散模型作为先验，允许仅在合成数据上进行训练的深度估计模型在真实图像上得到泛化；引入辅助表面法线损失进一步改进深度估计，模型对深度估计的置信度进行可靠预测，且在复杂自然场景的标准基准上，轻量级的方法表现优秀且计算成本低。