任意条件下的深度感知

摘要

我们推出了Depth Anything at Any Condition（DepthAnything-AC），这是一个基础的单目深度估计（MDE）模型，能够应对多样化的环境条件。以往的基础MDE模型在一般场景下表现卓越，但在包含复杂开放世界环境的挑战性条件下，如光照变化、恶劣天气及传感器引起的失真，则表现欠佳。为克服数据稀缺及从受损图像生成高质量伪标签的难题，我们提出了一种无监督一致性正则化微调范式，仅需相对少量的未标注数据。此外，我们引入了空间距离约束，明确促使模型学习块级相对关系，从而获得更清晰的语义边界和更精确的细节。实验结果表明，DepthAnything-AC在包括真实世界恶劣天气基准、合成失真基准及通用基准在内的多样化测试集上展现了卓越的零样本能力。项目页面：https://ghost233lism.github.io/depthanything-AC-page 代码仓库：https://github.com/HVision-NKU/DepthAnythingAC

English

We present Depth Anything at Any Condition (DepthAnything-AC), a foundation monocular depth estimation (MDE) model capable of handling diverse environmental conditions. Previous foundation MDE models achieve impressive performance across general scenes but not perform well in complex open-world environments that involve challenging conditions, such as illumination variations, adverse weather, and sensor-induced distortions. To overcome the challenges of data scarcity and the inability of generating high-quality pseudo-labels from corrupted images, we propose an unsupervised consistency regularization finetuning paradigm that requires only a relatively small amount of unlabeled data. Furthermore, we propose the Spatial Distance Constraint to explicitly enforce the model to learn patch-level relative relationships, resulting in clearer semantic boundaries and more accurate details. Experimental results demonstrate the zero-shot capabilities of DepthAnything-AC across diverse benchmarks, including real-world adverse weather benchmarks, synthetic corruption benchmarks, and general benchmarks. Project Page: https://ghost233lism.github.io/depthanything-AC-page Code: https://github.com/HVision-NKU/DepthAnythingAC