Pith Number

pith:WZBTSO2E

pith:2026:WZBTSO2E27RBUMDNPWGSYMN4LF

not attested not anchored not stored refs resolved

HarmoGS: Robust 3D Gaussian Splatting in the Wild via Conflict-Aware Gradient Harmonization

Jian-Fang Hu, Jianhuang Lai, Tianze Zhu, Wei-Shi Zheng, Yulei Kang

HarmoGS resolves conflicting gradients in wild 3D Gaussian Splatting by refining masks with semantic consistency and rotating view gradients into orthogonal alignment.

arxiv:2605.13073 v1 · 2026-05-13 · cs.CV

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\pithnumber{WZBTSO2E27RBUMDNPWGSYMN4LF}

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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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

We propose a conflict-aware 3DGS framework that addresses this problem from both image-space supervision and gradient-level optimization. ... our method achieves state-of-the-art rendering quality under complex transient distractors and cross-view inconsistencies.

C2weakest assumption

The assumption that semantic consistency-guided masking can accurately learn pixel-wise consistency scores to suppress unreliable supervision and that mutually rotating view-specific gradients into an orthogonal configuration reduces negative directional interference without losing useful optimization signal.

C3one line summary

HarmoGS improves in-the-wild 3D Gaussian Splatting by using semantic consistency-guided masking and dual-view conflict-aware gradient harmonization to reduce artifacts from transient distractors and cross-view inconsistencies.

References

37 extracted · 37 resolved · 1 Pith anchors

[1] Tensorf: Tensorial radiance fields 2022

[2] Nerf-hugs: Improved neural radiance fields in non-static scenes using heuristics-guided segmentation 2024

[3] Hallucinated neural radiance fields in the wild 2022

[4] K-planes: Explicit radiance fields in space, time, and appearance 2023

[5] Plenoxels: Radiance fields without neural networks 2022

Receipt and verification

First computed	2026-05-18T03:08:58.834878Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

b643393b44d7e21a306d7d8d2c31bc59731ac49967da5eabc6ebe8e091ea48f4

Aliases

arxiv: 2605.13073 · arxiv_version: 2605.13073v1 · doi: 10.48550/arxiv.2605.13073 · pith_short_12: WZBTSO2E27RB · pith_short_16: WZBTSO2E27RBUMDN · pith_short_8: WZBTSO2E

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/WZBTSO2E27RBUMDNPWGSYMN4LF \
  | 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: b643393b44d7e21a306d7d8d2c31bc59731ac49967da5eabc6ebe8e091ea48f4

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "159369d66e11c530893ed09f22456417af4ed4e9e4dea881f5cbf7eb23974f6c",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-13T06:47:09Z",
    "title_canon_sha256": "1db5eef78e2352e503b6cf4a096023be90291a84d2d6f075d5f1aed47c13f574"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13073",
    "kind": "arxiv",
    "version": 1
  }
}