Foundation-Agnostic Metric Depth Refinement via Sparse Semantic Guidance

Recent advancements in depth estimation rely heavily on powerful foundation models. While these massive networks capture robust scene representations, their real-world applications still require sparse depth guidance to resolve absolute scale and local precision. However, integrating this guidance remains highly problematic because existing refinement paradigms suffer from severe architectural entanglement. They are tightly coupled to specific network designs and require costly retraining for any new foundation architecture. To break this barrier, we present FreeDepth, the first universally decoupled framework for foundation-agnostic metric depth refinement. Our method achieves true decoupling by mapping sparse inputs into a universal semantic space through anchor-relative log-depth representations. This unified signal then seamlessly modulates the shared spatial topology of any heterogeneous network without relying on specific architectural priors. To guarantee absolute robustness against localized feed-forward errors, this decoupled pipeline is complemented by test-time optimization gated by gradient compatibility, while keeping all primary weights strictly frozen. Extensive experiments across five datasets demonstrate that FreeDepth establishes a new state-of-the-art performance. Remarkably, despite being trained solely via frozen interfaces of DA3 and InfiniDepth, the identical corrector demonstrates strong zero-shot transferability to unseen foundation models, such as ${\pi }^3$ and VGGT. All code and models will be publicly released.