Pith Number

pith:3MJ2LZQ2

pith:2026:3MJ2LZQ26LP77YZ2VJBPFWU5CJ

not attested not anchored not stored refs resolved

PanoPlane: Plane-Aware Panoramic Completion for Sparse-View Indoor 3D Gaussian Splatting

Adil Qureshi, Dinesh Manocha, Dongki Jung, Jaehoon Choi

PanoPlane achieves high-fidelity indoor novel view synthesis from sparse inputs by using plane-aware panoramic completion to supervise 3D Gaussian Splatting.

arxiv:2605.14135 v1 · 2026-05-13 · cs.CV

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\usepackage{pith}
\pithnumber{3MJ2LZQ26LP77YZ2VJBPFWU5CJ}

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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

Experiments on Replica, ScanNet++, and Matterport3D demonstrate state-of-the-art novel view synthesis quality across 3, 6, and 9 input views, achieving up to +17.8% improvement in PSNR over the current state-of-the-art baseline without any training or fine-tuning of the diffusion model.

C2weakest assumption

That steering attention in the diffusion model toward detected planar surfaces at inference time will reliably produce geometrically consistent extrapolations in unobserved regions without artifacts or inconsistencies.

C3one line summary

PanoPlane achieves up to 17.8% PSNR gains in sparse-view indoor novel view synthesis by using training-free plane-aware panoramic completion to supervise 3D Gaussian Splatting.

References

71 extracted · 71 resolved · 5 Pith anchors

[1] Self-rectifying diffusion sampling with perturbed-attention guidance 2024 · doi:10.1007/978-3-031-73464-9_1

[2] Qwen3-VL Technical Report 2025 · arXiv:2511.21631

[3] Masactrl: Tuning-free mutual self-attention control for consistent image synthesis and editing 2023

[4] Matterport3d: Learning from rgb-d data in indoor environments 2017 · arXiv:1709.06158

[5] Quantifying and alleviating co-adaptation in sparse-view 3d gaussian splatting 2025

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-17T23:39:11.749141Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

db13a5e61af2dfffe33aaa42f2da9d1262040214d713a77aec85e52167c70f6b

Aliases

arxiv: 2605.14135 · arxiv_version: 2605.14135v1 · doi: 10.48550/arxiv.2605.14135 · pith_short_12: 3MJ2LZQ26LP7 · pith_short_16: 3MJ2LZQ26LP77YZ2 · pith_short_8: 3MJ2LZQ2

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/3MJ2LZQ26LP77YZ2VJBPFWU5CJ \
  | 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: db13a5e61af2dfffe33aaa42f2da9d1262040214d713a77aec85e52167c70f6b

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "f766c0f2064cf7dcffa51f41a8b795bb59885b11690aac81775eb169558e4925",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-13T21:39:01Z",
    "title_canon_sha256": "f4c8a4dc3fa3fa6ffe1f0b5c9cd2328f50982f64031932abfd76ba376e96f87e"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.14135",
    "kind": "arxiv",
    "version": 1
  }
}