Pith Number

pith:65H2OCJH

pith:2026:65H2OCJHYYIRELAG77DAIM6IWD

not attested not anchored not stored refs resolved

A Holistic Method for Superquadric Fitting Using Unsupervised Clustering Analysis

Mingyang Zhao, Sipu Ruan, Xiaohong Jia

Superquadric fitting to noisy point clouds is reframed as unsupervised clustering where surface samples serve as dynamic centroids and input points as members.

arxiv:2605.16779 v1 · 2026-05-16 · cs.CV · cs.AI

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Record completeness

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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

the clustering process with dynamic updates to centroid locations serves as a direct proxy for optimizing superquadric parameters, establishing a principled link between geometric fitting and clustering dynamics

C2weakest assumption

That the relationship between pairwise computations of clustering centroids and clustering members to orthogonal distances accurately eliminates the need for surface sampling without introducing approximation errors

C3one line summary

Introduces a clustering-based optimization technique for fitting superquadrics to point clouds that handles noise, outliers, and deformations with closed-form solutions and convergence proofs.

References

54 extracted · 54 resolved · 2 Pith anchors

[1] Supervised fitting of geometric primitives to 3d point clouds, 2019

[2] Error of fit measures for recovering parametric solids, 1988

[3] Superquadrics with ra- tional and irrational symmetry, 2003

[4] Superquadric glyphs for sym- metric second-order tensors, 2010

[5] 3doodle: Compact abstraction of objects with 3d strokes, 2024

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:03:21.582387Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

f74fa70927c611122c06ffc60433c8b0f9ecf5a3caabde8a4cde3f0bf50f5ae4

Aliases

arxiv: 2605.16779 · arxiv_version: 2605.16779v1 · doi: 10.48550/arxiv.2605.16779 · pith_short_12: 65H2OCJHYYIR · pith_short_16: 65H2OCJHYYIRELAG · pith_short_8: 65H2OCJH

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/65H2OCJHYYIRELAG77DAIM6IWD \
  | 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: f74fa70927c611122c06ffc60433c8b0f9ecf5a3caabde8a4cde3f0bf50f5ae4

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "52e450610afdae0522ed824cbee8d6512cc47868acfedb0d988b374e450fa29c",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-16T03:20:33Z",
    "title_canon_sha256": "59c8eda067efb16ba095c8487851dabeea982e3d2a4b87a536cb97ed66212acc"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.16779",
    "kind": "arxiv",
    "version": 1
  }
}