Pith Number

pith:2XSBKUCL

pith:2024:2XSBKUCL2A4DHBLK6RL5XFRVN6

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Depth Pro: Sharp Monocular Metric Depth in Less Than a Second

Aleksei Bochkovskii, Ama\"el Delaunoy, Hugo Germain, Marcel Santos, Stephan R. Richter, Vladlen Koltun, Yichao Zhou

Depth Pro produces sharp, metric-scale depth maps from single images in 0.3 seconds without any camera metadata.

arxiv:2410.02073 v2 · 2024-10-02 · cs.CV · cs.LG

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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Claims

C1strongest claim

We present a foundation model for zero-shot metric monocular depth estimation. Our model, Depth Pro, synthesizes high-resolution depth maps with unparalleled sharpness and high-frequency details. The predictions are metric, with absolute scale, without relying on the availability of metadata such as camera intrinsics. And the model is fast, producing a 2.25-megapixel depth map in 0.3 seconds on a standard GPU.

C2weakest assumption

That the training protocol combining real and synthetic datasets, together with the multi-scale vision transformer, achieves both high metric accuracy and fine boundary tracing in zero-shot settings without camera intrinsics.

C3one line summary

Depth Pro is a fast foundation model for zero-shot metric monocular depth estimation that produces sharp high-resolution depth maps with absolute scale using a multi-scale vision transformer.

References

294 extracted · 294 resolved · 0 Pith anchors

[1] Defocus deblurring using dual-pixel data , author=. ECCV , year=

[2] RCA engineer , year=

[3] Attention Attention Everywhere: Monocular Depth Prediction with Skip Attention , author=. 2022 , journal= 2022

[4] Unilmv2: Pseudo-masked language models for unified language model pre-training , author=. ICML , year=

[5] Hangbo Bao and Li Dong and Songhao Piao and Furu Wei , booktitle=

Formal links

2 machine-checked theorem links

Cited by

42 papers in Pith

DissolveStereo: Coarse Depth Injection for Zero-Shot Stereo Video Generation

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation

GuideDog: A Real-World Egocentric Multimodal Dataset for Blind and Low-Vision Accessibility-Aware Guidance

H-Flow: Self-supervised Human Scene Flow via Physics-inspired Joint Multi-modal Learning

ROVR-Open-Dataset: A Large-Scale Depth Dataset for Autonomous Driving

Receipt and verification

First computed	2026-05-17T23:39:21.793339Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

d5e415504bd03833856af457db96356fb94c70ee224f334b887d6dd2ef091141

Aliases

arxiv: 2410.02073 · arxiv_version: 2410.02073v2 · doi: 10.48550/arxiv.2410.02073 · pith_short_12: 2XSBKUCL2A4D · pith_short_16: 2XSBKUCL2A4DHBLK · pith_short_8: 2XSBKUCL

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/2XSBKUCL2A4DHBLK6RL5XFRVN6 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: d5e415504bd03833856af457db96356fb94c70ee224f334b887d6dd2ef091141

Canonical record JSON

{
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    "abstract_canon_sha256": "931d920492db2ceb4a27ef60a79cecca7c630a75ffa3434b62011323ef254ab2",
    "cross_cats_sorted": [
      "cs.LG"
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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2024-10-02T22:42:20Z",
    "title_canon_sha256": "386f60539b565a925b362e7b02c790441f84bb996769af0d6bdfc7142633c60d"
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