REVIEW 5 cited by
DepthFM: Fast Monocular Depth Estimation with Flow Matching
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Current discriminative depth estimation methods often produce blurry artifacts, while generative approaches suffer from slow sampling due to curvatures in the noise-to-depth transport. Our method addresses these challenges by framing depth estimation as a direct transport between image and depth distributions. We are the first to explore flow matching in this field, and we demonstrate that its interpolation trajectories enhance both training and sampling efficiency while preserving high performance. While generative models typically require extensive training data, we mitigate this dependency by integrating external knowledge from a pre-trained image diffusion model, enabling effective transfer even across differing objectives. To further boost our model performance, we employ synthetic data and utilize image-depth pairs generated by a discriminative model on an in-the-wild image dataset. As a generative model, our model can reliably estimate depth confidence, which provides an additional advantage. Our approach achieves competitive zero-shot performance on standard benchmarks of complex natural scenes while improving sampling efficiency and only requiring minimal synthetic data for training.
Forward citations
Cited by 5 Pith papers
-
Dens3R: A Foundation Model for 3D Geometry Prediction
Dens3R jointly regresses pointmaps, depth, normals, and image matching from unposed images using a two-stage transformer training with normal-based intrinsic invariance.
-
E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models
E3D-Bench compares 16 3D geometric foundation models on depth, reconstruction, pose, and view-synthesis tasks with a unified evaluation toolkit.
-
UniGeo: Taming Video Diffusion for Unified Consistent Geometry Estimation
Fine-tuning a pretrained video diffusion transformer to predict geometry in one shared global frame produces consistent, camera-free surface normals and coordinates across entire video clips.
-
Region-aware Depth Scale Adaptation with Sparse Measurements
A non-learning method segments an image and gives each region its own scale and shift, fitted to a few sparse depth points, to turn relative monocular depth predictions into metric depth more accurately than a single ...
-
M3Depth: Wavelet-Enhanced Depth Estimation on Mars via Mutual Boosting of Dual-Modal Data
A wavelet-enhanced stereo network that combines low-frequency feature preservation with depth-normal consistency reports lower depth error than prior stereo models on a synthetic Mars dataset, with qualitative success...
Discussion (0). Continue with ORCID to comment.