任何條件下的深度萬物

摘要

我們提出了Depth Anything at Any Condition（DepthAnything-AC），這是一個能夠應對多樣環境條件的基礎單目深度估計（MDE）模型。以往的基礎MDE模型在一般場景中表現出色，但在涉及照明變化、惡劣天氣和傳感器引起失真等挑戰性條件的複雜開放世界環境中表現不佳。為克服數據稀缺和無法從損壞圖像生成高質量偽標籤的挑戰，我們提出了一種無監督一致性正則化微調範式，僅需相對少量的未標記數據。此外，我們提出了空間距離約束，明確要求模型學習圖塊級別的相對關係，從而獲得更清晰的語義邊界和更精確的細節。實驗結果展示了DepthAnything-AC在多樣基準測試中的零樣本能力，包括真實世界的惡劣天氣基準、合成損壞基準和一般基準。項目頁面：https://ghost233lism.github.io/depthanything-AC-page 代碼：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