Re-Depth Anything: Test-Time Depth Refinement via Self-Supervised Re-lighting

• URL: http://arxiv.org/abs/2512.17908v1
• Date: Fri, 19 Dec 2025 18:59:56 GMT
• Title: Re-Depth Anything: Test-Time Depth Refinement via Self-Supervised Re-lighting
• Authors: Ananta R. Bhattarai, Helge Rhodin,
• Abstract summary: We introduce Re-Depth Anything, a test-time self-supervision framework.<n>We fuse DA-V2 with the powerful priors of large-scale 2D diffusion models.<n>Our method performs label-free refinement directly on the input image.
• Score: 14.53203375098443
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Monocular depth estimation remains challenging as recent foundation models, such as Depth Anything V2 (DA-V2), struggle with real-world images that are far from the training distribution. We introduce Re-Depth Anything, a test-time self-supervision framework that bridges this domain gap by fusing DA-V2 with the powerful priors of large-scale 2D diffusion models. Our method performs label-free refinement directly on the input image by re-lighting predicted depth maps and augmenting the input. This re-synthesis method replaces classical photometric reconstruction by leveraging shape from shading (SfS) cues in a new, generative context with Score Distillation Sampling (SDS). To prevent optimization collapse, our framework employs a targeted optimization strategy: rather than optimizing depth directly or fine-tuning the full model, we freeze the encoder and only update intermediate embeddings while also fine-tuning the decoder. Across diverse benchmarks, Re-Depth Anything yields substantial gains in depth accuracy and realism over the DA-V2, showcasing new avenues for self-supervision by augmenting geometric reasoning.

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