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.

翻译：单目深度估计对于众多下游视觉任务与应用至关重要。当前判别式方法因模糊伪影而受限，而最先进的生成式方法则因随机微分方程（SDE）特性导致采样速度缓慢。我们不从噪声出发，而是寻求从输入图像到深度图的直接映射。研究发现，这一问题可通过流匹配有效构建——其通过解空间的直线轨迹兼具高效性与高质量。本研究证明，预训练图像扩散模型可作为流匹配深度模型的合适先验，使模型仅通过合成数据训练即可泛化至真实图像。我们还发现，辅助表面法线损失可进一步优化深度估计结果。由于方法的生成式特性，模型能可靠地预测其深度估计的置信度。在复杂自然场景的标准基准测试中，尽管仅基于少量合成数据训练，我们的轻量化方法仍以优越的低计算成本展现出最先进的性能。