Zero-Shot Depth from Defocus
Depth from Defocus (DfD) is the task of estimating a dense metric depth map from a focus stack. Unlike previous works overfitting to a certain dataset, this paper focuses on the challenging and practical setting of zero-shot generalization. We first propose a new real-world DfD benchmark ZEDD, which contains 8.3x more scenes and significantly higher quality images and ground-truth depth maps compared to previous benchmarks. We also design a novel network architecture named FOSSA. FOSSA is a Transformer-based architecture with novel designs tailored to the DfD task. The key contribution is a stack attention layer with a focus distance embedding, allowing efficient information exchange acro...
| Authors | Yiming Zuo, Hongyu Wen, Venkat Subramanian et al. |
|---|---|
| Primary category | cs.CV |
| Published | 27 March 2026 |
| Last updated at source | 27 June 2026 |
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- venkatsubra/ZEDDDataset
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