Recoverable Identifier

arXiv:2605.18545 · detector doi_compliance · incontrovertible · 2026-05-20 08:42:04.250137+00:00

advisory doi_compliance recoverable_identifier

DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1117/12.2654148.full.doi:10.1117/12.2654148.Weston) was visible in the surrounding text but could not be confirmed against doi.org as printed.

Paper page Integrity report arXiv Try DOI

Evidence text

C. Zhou, Y. Wang, X. Qi, S. Si, H. Li, Synthetic training datasets generating for fringe projection profilometry based on deep learning, in: Thirteenth International Conference on Information Optics and Photonics (CIOP 2022), volume 12478, SPIE, 2022, pp. 416–424. URL:https://www.spiedigitallibrary. org/conference-proceedings-of-spie/12478/124781N/ Synthetic-training-datasets-generating-for-fringe-projection-profilometry-based-on/ 10.1117/12.2654148.full. doi:10.1117/12.2654148. Weston et al.:Preprint submitted to ElsevierPage 10 of 10

Evidence payload

{
  "printed_excerpt": "C. Zhou, Y. Wang, X. Qi, S. Si, H. Li, Synthetic training datasets generating for fringe projection profilometry based on deep learning, in: Thirteenth International Conference on Information Optics and Photonics (CIOP 2022), volume 12478, ",
  "reconstructed_doi": "10.1117/12.2654148.full.doi:10.1117/12.2654148.Weston",
  "ref_index": 30,
  "resolved_title": null,
  "verdict_class": "incontrovertible"
}