UniDAC：面向任意相机的通用度量深度估计
UniDAC: Universal Metric Depth Estimation for Any Camera
把度量深度解耦为相对深度预测加空间变化的尺度估计，并用相对深度引导的轻量模块把粗尺度图上采样到高分辨率，这一思路可直接迁移到鱼眼、全景等大视场相机的深度模型；RoPE-φ 的纬度感知加权位置编码也为 ERP 畸变建模提供了可复用的做法。
原文依据 decoupling metric depth estimation into relative depth prediction and spatially varying scale estimation
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论单目度量深度估计（MMDE）是计算机视觉中的核心挑战，在需要精确空间理解的现实应用中起着关键作用。尽管已有工作在 MMDE 上展现出有前景的零样本性能，但它们往往难以泛化到鱼眼、360° 相机等多样化的相机类型。近期的进展通过统一的相机表示或规范表示空间来解决这一问题，但这些方法要么需要在训练时纳入大视场相机数据，要么需要针对不同域分别训练模型。我们提出 UniDAC，一个在所有域上都具备通用鲁棒性、并能用单一模型泛化到多样相机的 MMDE 框架。我们通过将度量深度估计解耦为相对深度预测与空间变化的尺度估计来实现这一点，从而在不同域上获得鲁棒性能。我们提出一个轻量的深度引导尺度估计模块，以相对深度图为引导将粗尺度图上采样到高分辨率，以刻画局部尺度变化。此外，我们引入 RoPE-φ，一种畸变感知的位置嵌入，通过纬度感知加权来尊重等距柱状投影（ERP）中的空间形变。UniDAC 在跨相机泛化上达到最先进水平，在所有数据集上持续优于先前方法。
英文原文摘要 1427 字符
Monocular metric depth estimation (MMDE) is a core challenge in computer vision, playing a pivotal role in real-world applications that demand accurate spatial understanding. Although prior works have shown promising zero-shot performance in MMDE, they often struggle with generalization across diverse camera types, such as fisheye and 360^\circ cameras. Recent advances have addressed this through unified camera representations or canonical representation spaces, but they require either including large FoV camera data during training or separately trained models for different domains. We propose UniDAC, an MMDE framework that presents universal robustness in all domains and generalizes across diverse cameras using a single model. We achieve this by decoupling metric depth estimation into relative depth prediction and spatially varying scale estimation, enabling robust performance across different domains. We propose a lightweight Depth-Guided Scale Estimation module that upsamples a coarse scale map to high resolution using the relative depth map as guidance to account for local scale variations. Furthermore, we introduce RoPE-\phi, a distortion-aware positional embedding that respects the spatial warping in Equi-Rectangular Projections (ERP) via latitude-aware weighting. UniDAC achieves state of the art (SoTA) in cross-camera generalization by consistently outperforming prior methods across all datasets.