The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation
Open accessAbstract
Monocular depth estimation is a fundamental computer vision task. Recovering 3D depth from a single image is geometrically ill-posed and requires scene understanding, so it is not surprising that the rise of deep learning has led to a breakthrough. The impressive progress of monocular depth estimators has mirrored the growth in model capacity, from relatively modest CNNs to large Transformer architectures. Still, monocular depth estimators tend to struggle when presented with images with unfamiliar content and layout, since their knowledge of the visual world is restricted by the data seen during training, and challenged by zero-shot generalization to new domains. This motivates us to explore whether the extensive priors captured in recent generative diffusion models can enable better, more generalizable depth estimation. We introduce Marigold, a method for affine-invariant monocular depth estimation that is derived from Stable Diffusion and retains its rich prior knowledge. The estimator can be fine-tuned in a couple of days on a single GPU using only synthetic training data. It delivers state-of-the-art performance across a wide range of datasets, including over 20% performance gains in specific cases. Project page: https://marigoldmonodepth.github.io.
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Cites
High-Resolution Image Synthesis with Latent Diffusion Models
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Conditional Generative Adversarial Nets
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WorldScore: A Unified Evaluation Benchmark for World Generation
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Metadata
- Authors
- Ke, B., Obukhov, A., Huang, S., Metzger, N., Daudt, R. C., & Schindler, K.
- Venue
- 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2024-06-16
- Pages
- 9492-9502
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4402753888
- Cited by
- 412
- References
- 57
- FWCI
- 89.96
- Type
- conference paper
- Language
- en
- Open access
- green
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- Ke, B., Obukhov, A., Huang, S., Metzger, N., Daudt, R. C., & Schindler, K. (2024). Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr52733.2024.00907
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