Pith Number

pith:YXSSY5HL

pith:2025:YXSSY5HLXIDGSADHJIAV2FJ7AH

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MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Details

Guangzhong Sun, Jianfeng Xiang, Jiaolong Yang, Ruicheng Wang, Sicheng Xu, Xin Tong, Yu Deng, Yue Dong, Zelong Lv

MoGe-2 recovers metric-scale 3D point maps from single images while preserving relative accuracy and recovering fine details.

arxiv:2507.02546 v1 · 2025-07-03 · cs.CV

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\usepackage{pith}
\pithnumber{YXSSY5HLXIDGSADHJIAV2FJ7AH}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

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

demonstrating its superior performance in achieving accurate relative geometry, precise metric scale, and fine-grained detail recovery -- capabilities that no previous methods have simultaneously achieved.

C2weakest assumption

That filtering and completing real data with sharp synthetic labels preserves overall accuracy without introducing systematic biases or artifacts in the metric scale prediction.

C3one line summary

MoGe-2 recovers metric-scale 3D point maps with fine details from single images via data refinement and extension of affine-invariant predictions.

References

83 extracted · 83 resolved · 7 Pith anchors

[1] Apollo synthetic dataset, 2019 2019

[2] Zip-nerf: Anti- aliased grid-based neural radiance fields 2023

[3] ARKitscenes - a diverse real-world dataset for 3d indoor scene understanding using mobile RGB-d data 2021

[4] Adabins: Depth estimation using adaptive bins 2021

[5] ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth 2023 · arXiv:2302.12288

Formal links

2 machine-checked theorem links

Cited by

33 papers in Pith

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AIR: Amortized Image Reconstruction Framework for Self-Supervised Feed-Forward 2D Gaussian Splatting

UniT: Unified Geometry Learning with Group Autoregressive Transformer

Receipt and verification

First computed	2026-05-17T23:39:21.645140Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

c5e52c74ebba066900674a015d153f01caa1089c8351a8eaac978b0d245c9b6a

Aliases

arxiv: 2507.02546 · arxiv_version: 2507.02546v1 · doi: 10.48550/arxiv.2507.02546 · pith_short_12: YXSSY5HLXIDG · pith_short_16: YXSSY5HLXIDGSADH · pith_short_8: YXSSY5HL

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/YXSSY5HLXIDGSADHJIAV2FJ7AH \
  | 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: c5e52c74ebba066900674a015d153f01caa1089c8351a8eaac978b0d245c9b6a

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "46d6c080246ee895c5b0b64f5fcd2d6b810ce47af3be30b136d59c0a34a3ec32",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2025-07-03T11:40:01Z",
    "title_canon_sha256": "69a4c31a1c4f93988564512ca314c607b2e13e3d74a903d61a230c5f492f3ee4"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2507.02546",
    "kind": "arxiv",
    "version": 1
  }
}