Pith Number

pith:TZJ2KCQW

pith:2026:TZJ2KCQWCTXWBXHVHYB7223MGS

not attested not anchored not stored refs resolved

VGGT-360: Geometry-Consistent Zero-Shot Panoramic Depth Estimation

De Wen Soh, Haobo Jiang, Jiayi Yuan, Na Zhao

VGGT-360 turns panoramic depth estimation into consistent 3D reprojection by leveraging VGGT foundation models without any training.

arxiv:2603.18943 v2 · 2026-03-19 · 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{TZJ2KCQWCTXWBXHVHYB7223MGS}

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

VGGT-360 outperforms both trained and training-free state-of-the-art methods across multiple resolutions and diverse indoor and outdoor datasets.

C2weakest assumption

That VGGT-like foundation models possess intrinsic 3D consistency sufficient to unify fragmented per-view reasoning into coherent panoramic depth via the proposed reprojection pipeline.

C3one line summary

VGGT-360 delivers geometry-consistent zero-shot panoramic depth by converting panoramas into multi-view 3D reconstructions via VGGT models and three plug-and-play correction modules, then reprojecting the result.

References

52 extracted · 52 resolved · 6 Pith anchors

[1] Elite360d: Towards efficient 360 depth estimation via semantic-and distance-aware bi- projection fusion 2024

[2] Hrdfuse: Monocular 360deg depth estimation by collaboratively learning holistic-with-regional depth distri- butions 2023

[3] Joint 2d-3d-semantic data for indoor scene understanding 2017 · arXiv:1702.01105

[4] Matryodshka: Real-time 6dof video view synthesis using multi-sphere images 2020

[5] Omniphotos: casual 360 vr photography.ACM Transactions on Graphics (TOG), 39(6):1–12, 2020 2020

Cited by

1 paper in Pith

Good Token Hunting: A Hitchhiker's Guide to Token Selection for Visual Geometry Transformers

Receipt and verification

First computed	2026-05-17T23:39:15.733252Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

9e53a50a1614ef60dcf53e03fd6b6c349b78e92ba4d3d786072be5e99c3728de

Aliases

arxiv: 2603.18943 · arxiv_version: 2603.18943v2 · doi: 10.48550/arxiv.2603.18943 · pith_short_12: TZJ2KCQWCTXW · pith_short_16: TZJ2KCQWCTXWBXHV · pith_short_8: TZJ2KCQW

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/TZJ2KCQWCTXWBXHVHYB7223MGS \
  | 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: 9e53a50a1614ef60dcf53e03fd6b6c349b78e92ba4d3d786072be5e99c3728de

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "2afbc35d5fae08204959d7267c30842dea132b8402fdbaf9c19ec1627b922f8e",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-03-19T14:18:17Z",
    "title_canon_sha256": "049151b81b25f7c0716f4c6edbd96d173665c76c46d6e4cbe73334223d841778"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2603.18943",
    "kind": "arxiv",
    "version": 2
  }
}