Pith Number

pith:T7ZZFOUO

pith:2025:T7ZZFOUO5OYYOO3TTYPI755A6W

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UniDepthV2: Universal Monocular Metric Depth Estimation Made Simpler

Christos Sakaridis, Luc Van Gool, Luigi Piccinelli, Mattia Segu, Siyuan Li, Wim Abbeloos, Yung-Hsu Yang

UniDepthV2 predicts metric 3D points directly from single images across domains without extra inputs or retraining.

arxiv:2502.20110 v2 · 2025-02-27 · cs.CV

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Record completeness

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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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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

UniDepthV2 is capable of reconstructing metric 3D scenes from solely single images across domains, improves its predecessor via edge-guided loss, simplified design, and uncertainty output, and shows superior zero-shot performance on ten depth datasets.

C2weakest assumption

The self-promptable camera module and geometric invariance loss can reliably disentangle and generalize camera and depth features without domain-specific information or post-hoc adjustments.

C3one line summary

UniDepthV2 predicts metric 3D points directly from single images using a self-promptable camera module, pseudo-spherical representation, and new losses for improved cross-domain generalization.

References

90 extracted · 90 resolved · 4 Pith anchors

[1] Depth-supervised nerf: Fewer views and faster training for free, 2022

[2] Does computer vision matter for action? 2019

[3] Towards real-time monocular depth estimation for robotics: A survey, 2022

[4] Pseudo-lidar from visual depth estimation: Bridging the gap in 3d object detection for autonomous driving, 2019

[5] Is pseudo-lidar needed for monocular 3d object detection? 2021

Formal links

2 machine-checked theorem links

Cited by

20 papers in Pith

SpaceDrive: Infusing Spatial Awareness into VLM-based Autonomous Driving

HumanSplatHMR: Closing the Loop Between Human Mesh Recovery and Gaussian Splatting Avatar

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

ViPE: Video Pose Engine for 3D Geometric Perception

RAD: Retrieval-Augmented Monocular Metric Depth Estimation for Underrepresented Classes

Receipt and verification

First computed	2026-05-17T23:38:14.500017Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

9ff392ba8eebb1873b739e1e8ff7a0f58135acb7684e5745e21045aa8f19539c

Aliases

arxiv: 2502.20110 · arxiv_version: 2502.20110v2 · doi: 10.48550/arxiv.2502.20110 · pith_short_12: T7ZZFOUO5OYY · pith_short_16: T7ZZFOUO5OYYOO3T · pith_short_8: T7ZZFOUO

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/T7ZZFOUO5OYYOO3TTYPI755A6W \
  | 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: 9ff392ba8eebb1873b739e1e8ff7a0f58135acb7684e5745e21045aa8f19539c

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "e9a08e1f1d555ae397933a1837c99b6d2833f6cc5b0c1eea97f923d444200fa7",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2025-02-27T14:03:15Z",
    "title_canon_sha256": "68a0fcd2de0d339511f8281b97df677821d623c66641a7b3566c8e9a30afa586"
  },
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  "source": {
    "id": "2502.20110",
    "kind": "arxiv",
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}