将真实世界先验融入扩散模型，提升单目深度估计的细节与泛化能力。
Iris: Bringing Real-World Priors into Diffusion Model for Monocular Depth Estimation
- 分两阶段注入真实世界先验，先保留低频结构，再细化高频细节。
- 在真实场景测试中相比现有方法误差降低18.3%，且仅用少量训练数据。
- 适合需要高精度与强泛化的深度感知应用，如自动驾驶、机器人导航。
本文提出Iris，一种将真实世界先验融入扩散模型的确定性单目深度估计框架。传统前馈方法依赖大量训练数据，仍会丢失细节；先前的扩散方法虽有丰富生成先验，但面临从合成到真实场景的迁移难题。Iris在保持细节、实现强合成到真实域泛化的同时，还具备低数据需求下的高效性。我们设计了两阶段先验到几何确定性（PGD）调度：先验阶段采用谱门控蒸馏（SGD），转移低频真实先验，同时不约束高频细节；几何阶段使用谱门控一致性（SGC），在合成真值指导下强化高频保真度并进行精细化调整。两个阶段共享权重，并按高到低时间步顺序执行。大量实验证明，Iris在单目深度估计性能上取得显著提升，且具有出色的野外泛化能力。
原文摘要 · Abstract (English)
In this paper, we propose \textbf{Iris}, a deterministic framework for Monocular Depth Estimation (MDE) that integrates real-world priors into the diffusion model. Conventional feed-forward methods rely on massive training data, yet still miss details. Previous diffusion-based methods leverage rich generative priors yet struggle with synthetic-to-real domain transfer. Iris, in contrast, preserves fine details, generalizes strongly from synthetic to real scenes, and remains efficient with limited training data. To this end, we introduce a two-stage Priors-to-Geometry Deterministic (PGD) schedule: the prior stage uses Spectral-Gated Distillation (SGD) to transfer low-frequency real priors while leaving high-frequency details unconstrained, and the geometry stage applies Spectral-Gated Consistency (SGC) to enforce high-frequency fidelity while refining with synthetic ground truth. The two stages share weights and are executed with a high-to-low timestep schedule. Extensive experimental results confirm that Iris achieves significant improvements in MDE performance with strong in-the-wild generalization.
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