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pith:CCEIAIEU

pith:2026:CCEIAIEUH2ZVQBWANY5XLWVYJS

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VGGT-CD: Training-Free Robust Registration for 3D Change Detection

China), Qiang Li (1), Qi Wang (1) ((1) Northwestern Polytechnical University, Songhua Li (1), Wei Zhang (1), Xi'an, Yihang Wu (1)

VGGT-CD registers multi-temporal point clouds by first aligning sparse keyframes into one metric space then purifying dense reconstructions to static background only.

arxiv:2605.16859 v1 · 2026-05-16 · cs.CV · cs.AI

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Claims

C1strongest claim

Evaluated on an 11-scene benchmark from the World Across Time dataset, VGGT-CD reduces Absolute Trajectory Error by 44% outdoors and 59% indoors. It completes registration over 6 times faster, producing high-purity 3D change maps without task-specific training.

C2weakest assumption

The fine stage can reliably isolate static-background correspondences from dynamic-change interference so that centroid alignment on the remaining points yields a non-degrading refinement; this premise is invoked when the paper states that dense reconstructions are purified by isolating static-background correspondences.

C3one line summary

VGGT-CD decouples cross-temporal registration from dynamic changes using VGGT reconstructions, achieving 44% and 59% lower Absolute Trajectory Error outdoors and indoors on an 11-scene benchmark while running over 6 times faster.

References

40 extracted · 40 resolved · 1 Pith anchors

[1] Change detection of urban objects using 3D point clouds: A review 2023

[2] Change detection in urban point clouds: An experimental comparison with simulated 3d datasets 2021

[3] Point cloud registration and change detection in urban environ- ment using an onboard Lidar sensor and MLS reference data 2022

[4] Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: From natural disasters to man- made disasters 2021

[5] Integrating machine learning and remote sensing in disaster management: A decadal review of post-disaster building damage assessment 2024

Receipt and verification

First computed	2026-05-20T00:03:26.702853Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

10888020943eb35806c06e3b75dab84c849a5cb663214645563c8f8ea8dd9de0

Aliases

arxiv: 2605.16859 · arxiv_version: 2605.16859v1 · doi: 10.48550/arxiv.2605.16859 · pith_short_12: CCEIAIEUH2ZV · pith_short_16: CCEIAIEUH2ZVQBWA · pith_short_8: CCEIAIEU

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/CCEIAIEUH2ZVQBWANY5XLWVYJS \
  | 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: 10888020943eb35806c06e3b75dab84c849a5cb663214645563c8f8ea8dd9de0

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "d4741413f67539c2f099c609055fb9d53df402c356ced88874bfed36f9bb574c",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-16T07:44:04Z",
    "title_canon_sha256": "ecac141bcb0d8ffa97ffea30dd2fd4794e9cd21ec64e40ee83bf2a4d70fe5a41"
  },
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  "source": {
    "id": "2605.16859",
    "kind": "arxiv",
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  }
}