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TEXMET: A Deep Learning Benchmark for Archaeological Textile Reconstruction — arXiv:2611.04734

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.04734
License (as archived): CC0-1.0
Archived: 2026-11-04T12:37:13.059Z
SHA-256 of the archived text: 9d6a2917a56e7265c2ac75f99d1f7c42ee348ce7165d95ed442926324e8db3a7
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How to cite

TEXMET: A Deep Learning Benchmark for Archaeological Textile Reconstruction — arXiv:2611.04734 — arXiv (en), https://arxiv.org/abs/2611.04734 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_e23052bf32ff (Metraipolis archive dc_e23052bf32ff, sha256 9d6a2917a56e7265c2ac75f99d1f7c42ee348ce7165d95ed442926324e8db3a7)

BibTeX

@misc{dce23052bf32ff,
  title = {TEXMET: A Deep Learning Benchmark for Archaeological Textile Reconstruction — arXiv:2611.04734},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.04734},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_e23052bf32ff; sha256 9d6a2917a56e7265c2ac75f99d1f7c42ee348ce7165d95ed442926324e8db3a7},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.04734. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

The reconstruction of fragmented historical textiles presents a significant challenge in computational archaeology. The Oseberg ship burial, dating to 834 CE, yielded hundreds of degraded textile fragments that have resisted systematic computational analysis. While deep learning has revolutionized image-based puzzle solving, progress on archaeological textiles has been limited by the lack of domain-specific datasets and systematic evaluation of modern architectures. To address this gap, we present TEXMET, a curated dataset of 18,644 historical textile images from the Metropolitan Museum of Art's Open Access collection, representing the first textile-specific benchmark for puzzle-solving research. Using TEXMET, we conduct a systematic evaluation of three state-of-the-art puzzle-solving models: JPDVT, FCViT, and DiffAssemble. Our experiments reveal that the transformer-based models pre-trained on standard datasets fail to generalize to textiles, but domain-specific fine-tuning proves highly effective. The graph-based DiffAssemble model, when fine-tuned on TEXMET, achieves 85.89% puzzle accuracy on textile data. Most significantly, applying this fine-tuned model to 45 highly degraded Oseberg fragments yields a performance increase from 26.67% to 64.44% puzzle accuracy. These results establish TEXMET as a new benchmark for textile reconstruction and demonstrate that domain adaptation is essential for applying puzzle-solving methods to imagery of real archaeological materials.

Tema degli esploratori: ricerca e scienza aperta (elenco arXiv cs.DL, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Digital Libraries (cs.DL).
Pagina del lavoro: https://arxiv.org/abs/2611.04734 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-04T12:28:47.095Z dalla pagina https://arxiv.org/list/cs.DL/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).