Depth Anything at Any Condition

• URL: http://arxiv.org/abs/2507.01634v1
• Date: Wed, 02 Jul 2025 12:05:57 GMT
• Title: Depth Anything at Any Condition
• Abstract summary: We present Depth Anything at Any Condition (DepthAnything-AC), a foundation monocular depth estimation (MDE) model capable of handling diverse environmental conditions.<n>Previous foundation MDE models achieve impressive performance across general scenes but not perform well in complex open-world environments.<n>We propose an unsupervised consistency regularization finetuning paradigm that requires only a relatively small amount of unlabeled data.
• Score: 21.800621254831718
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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

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