Pith Number

pith:ADLVAHWM

pith:2026:ADLVAHWMNRVSMSHKK7T7GFP77Q

not attested not anchored not stored refs resolved

SceneForge: Structured World Supervision from 3D Interventions

Danny Wicks, Jiayang Ao, Jizhizi Li, Petru-Daniel Tudosiu

SceneForge generates consistent supervision for removal tasks by propagating explicit interventions through editable 3D scene states.

arxiv:2605.14399 v1 · 2026-05-14 · cs.CV · cs.GR

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{ADLVAHWMNRVSMSHKK7T7GFP77Q}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

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

Under matched training budgets, incorporating SceneForge supervision improves both object removal and scene removal performance across multiple benchmarks in both quantitative and qualitative evaluation.

C2weakest assumption

That supervision generated from synthetic 3D interventions transfers effectively to real-world images and that the modeled scene dependencies accurately reflect real physical and geometric relationships.

C3one line summary

SceneForge creates intervention-consistent multimodal supervision from editable 3D world states, yielding improved object and scene removal performance on benchmarks.

References

38 extracted · 38 resolved · 0 Pith anchors

[1] Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , year =

[2] Proceedings of the IEEE/CVF International Conference on Computer Vision , year=

[3] Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , year =

[4] Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

[5] Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track , year=

Receipt and verification

First computed	2026-05-17T23:39:07.506993Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

00d7501ecc6c6b2648ea57e7f315fffc17bece3a4864e4c0fe756112601fd673

Aliases

arxiv: 2605.14399 · arxiv_version: 2605.14399v1 · doi: 10.48550/arxiv.2605.14399 · pith_short_12: ADLVAHWMNRVS · pith_short_16: ADLVAHWMNRVSMSHK · pith_short_8: ADLVAHWM

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/ADLVAHWMNRVSMSHKK7T7GFP77Q \
  | 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: 00d7501ecc6c6b2648ea57e7f315fffc17bece3a4864e4c0fe756112601fd673

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "30288ca1efb73e2f4ec73a851b1bd96fc43628d80ec76c21b22b7298d2de0c69",
    "cross_cats_sorted": [
      "cs.GR"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-14T05:38:00Z",
    "title_canon_sha256": "dbaa404ffd61e130b8d6f8b6a752ee975c7b875174344365388aac52b44bf1ed"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.14399",
    "kind": "arxiv",
    "version": 1
  }
}