Papers › HybridDepth: Robust Metric Depth Fusion by Leveraging Depth from Focus and Single-Image Priors
HybridDepth: Robust Metric Depth Fusion by Leveraging Depth from Focus and Single-Image Priors
Ashkan Ganj, Hang Su, Tian Guo
We propose HYBRIDDEPTH, a robust depth estimation pipeline that addresses key challenges in depth estimation,including scale ambiguity, hardware heterogeneity, and generalizability. HYBRIDDEPTH leverages focal stack, data conveniently accessible in common mobile devices, to produce accurate metric depth maps. By incorporating depth priors afforded by recent advances in singleimage depth estimation, our model achieves a higher level of structural detail compared to existing methods. We test our pipeline as an end-to-end system, with a newly developed mobile client to capture focal stacks, which are then sent to a GPU-powered server for depth estimation. Comprehensive quantitative and qualitative analyses demonstrate that HYBRIDDEPTH outperforms state-of-the-art(SOTA) models on common datasets such as DDFF12 and NYU Depth V2. HYBRIDDEPTH also shows strong zero-shot generalization. When trained on NYU Depth V2, HYBRIDDEPTH surpasses SOTA models in zero-shot performance on ARKitScenes and delivers more structurally accurate depth maps on Mobile Depth. The code is available at https://github.com/cake-lab/HybridDepth/.
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Tasks
1 archive task tag without a task page not shown.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Monocular Depth Estimation | NYU-Depth V2 | HybridDepth | Delta < 1.25 | 0.988 | #1 of 85 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 | HybridDepth | Delta < 1.25^2 | 1.000 | #1 of 85 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 | HybridDepth | Delta < 1.25^3 | 1.000 | #1 of 85 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 | HybridDepth | RMSE | 0.128 | #1 of 85 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 | HybridDepth | absolute relative error | 0.026 | #1 of 85 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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