Pith Number

pith:CN3MTWMI

pith:2026:CN3MTWMIG4TKFS2ESCMPO62AH2

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Aes3D: Aesthetic Assessment in 3D Gaussian Splatting

Boyu Wei, Chuanzhi Xu, Haodong Chen, Haoxian Zhou, Qiang Qu, Weidong Cai, Xuanhua Yin, Zihan Deng

A lightweight model predicts aesthetic scores for 3D scenes directly from Gaussian splat primitives without rendering images.

arxiv:2605.05155 v2 · 2026-05-06 · cs.CV · cs.AI

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Claims

C1strongest claim

we propose Aes3D, the first systematic framework for assessing the aesthetics of 3D neural rendering scenes. Aes3D includes Aesthetic3D, the first dataset dedicated to 3D scene aesthetic assessment... In addition, we present Aes3DGSNet, a lightweight model that directly predicts scene-level aesthetic scores from 3DGS representations... Experimental results demonstrate that our approach achieves strong performance while maintaining a lightweight design, establishing a new benchmark for 3D scene aesthetic assessment.

C2weakest assumption

That high-level aesthetic attributes such as composition, harmony, and visual appeal can be captured and accurately regressed from low-level 3D Gaussian primitives alone, without rendering multi-view images, using a lightweight network trained via aesthetics-supervised learning on multi-view 3DGS representations.

C3one line summary

Aes3D creates the first dedicated dataset for 3D scene aesthetics and a model that predicts aesthetic scores straight from 3D Gaussian primitives.

Receipt and verification

First computed	2026-05-26T01:03:32.295434Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

1376c9d9883726a2cb449098f77b403ebbbeed9d86ed4b3d0f49e6e6f793d90e

Aliases

arxiv: 2605.05155 · arxiv_version: 2605.05155v2 · doi: 10.48550/arxiv.2605.05155 · pith_short_12: CN3MTWMIG4TK · pith_short_16: CN3MTWMIG4TKFS2E · pith_short_8: CN3MTWMI

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/CN3MTWMIG4TKFS2ESCMPO62AH2 \
  | 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: 1376c9d9883726a2cb449098f77b403ebbbeed9d86ed4b3d0f49e6e6f793d90e

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "3cfb8664d8d4e1e9c720866d64879f4ed132b6c9a4e97401ccce46540de4c38f",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by-sa/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-06T17:27:09Z",
    "title_canon_sha256": "ab3ff04493f28cdf90b4f2ff3b00929fadd08d185c34c300eb3509c6af3fc479"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.05155",
    "kind": "arxiv",
    "version": 2
  }
}