Pith Number

pith:YIDJOLNE

pith:2026:YIDJOLNEKGUVWCFKAWHJQ7IFG3

not attested not anchored not stored refs pending

Geometry Reinforced Efficient Attention Tuning Equipped with Normals for Robust Stereo Matching

Cheng Huang, Jiahao Li, Jianping Wang, Xinhong Chen, Yung-Hui Li, Zhengmin Jiang

Surface normals provide domain-invariant geometric cues that improve zero-shot generalization in stereo matching from synthetic to real data.

arxiv:2604.09142 v2 · 2026-04-10 · cs.CV

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

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{YIDJOLNEKGUVWCFKAWHJQ7IFG3}

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

Trained exclusively on synthetic data such as SceneFlow, GREATEN-IGEV achieves outstanding Syn-to-Real performance. Specifically, it reduces errors by 30% on ETH3D, 8.5% on the non-Lambertian Booster, and 14.1% on KITTI-2015, compared to FoundationStereo, Monster-Stereo, and DEFOM-Stereo, respectively.

C2weakest assumption

That surface normals can be obtained or estimated reliably enough in real scenes to serve as domain-invariant cues, and that the gated fusion module will consistently suppress misleading image textures without introducing new artifacts in occluded or non-Lambertian regions.

C3one line summary

GREATEN fuses surface normals with image features via gated contextual-geometric fusion and efficient sparse attentions to cut stereo matching errors by up to 30% on real datasets when trained solely on synthetic data.

Receipt and verification

First computed	2026-06-30T02:17:20.478836Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

c206972da451a95b08aa058e987d0536eb0e77ceff439dc6777ecdc3fd0acc14

Aliases

arxiv: 2604.09142 · arxiv_version: 2604.09142v2 · doi: 10.48550/arxiv.2604.09142 · pith_short_12: YIDJOLNEKGUV · pith_short_16: YIDJOLNEKGUVWCFK · pith_short_8: YIDJOLNE

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/YIDJOLNEKGUVWCFKAWHJQ7IFG3 \
  | 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: c206972da451a95b08aa058e987d0536eb0e77ceff439dc6777ecdc3fd0acc14

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "3280ebc5d0db3aba2b8188bd53a6d39ece7ab10aa9c4cd54af9b40267cbd167b",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-04-10T09:21:51Z",
    "title_canon_sha256": "4b257d9f8995d2c0f483a01713d7caedbc60573071ed65d383a6df38455acf37"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.09142",
    "kind": "arxiv",
    "version": 2
  }
}