Papers › Zero-Shot Metric Depth with a Field-of-View Conditioned Diffusion Model
Zero-Shot Metric Depth with a Field-of-View Conditioned Diffusion Model
Saurabh Saxena, Junhwa Hur, Charles Herrmann, Deqing Sun, David J. Fleet
While methods for monocular depth estimation have made significant strides on standard benchmarks, zero-shot metric depth estimation remains unsolved. Challenges include the joint modeling of indoor and outdoor scenes, which often exhibit significantly different distributions of RGB and depth, and the depth-scale ambiguity due to unknown camera intrinsics. Recent work has proposed specialized multi-head architectures for jointly modeling indoor and outdoor scenes. In contrast, we advocate a generic, task-agnostic diffusion model, with several advancements such as log-scale depth parameterization to enable joint modeling of indoor and outdoor scenes, conditioning on the field-of-view (FOV) to handle scale ambiguity and synthetically augmenting FOV during training to generalize beyond the limited camera intrinsics in training datasets. Furthermore, by employing a more diverse training mixture than is common, and an efficient diffusion parameterization, our method, DMD (Diffusion for Metric Depth) achieves a 25\% reduction in relative error (REL) on zero-shot indoor and 33\% reduction on zero-shot outdoor datasets over the current SOTA using only a small number of denoising steps. For an overview see https://diffusion-vision.github.io/dmd
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Monocular Depth Estimation | NYU-Depth V2 | DMD | Delta < 1.25 | 0.953 | #23 of 85 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 | DMD | Delta < 1.25^2 | 0.989 | #23 of 85 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 | DMD | Delta < 1.25^3 | 0.996 | #23 of 85 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 | DMD | RMSE | 0.296 | #23 of 85 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 | DMD | absolute relative error | 0.072 | #23 of 85 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 | DMD | log 10 | 0.031 | #23 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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