{
  "abstract": "Augmented Reality (AR) can enhance industrial processes like manual inspections by increasing safety and efficiency. This is achieved by superimposing virtual guidance directly onto the physical environment, such as visual highlights, step-by-step instructions, or animated tool paths. The accurate alignment of virtual guidance depends directly on the precise registration of real-world components. This study evaluates the 6D pose estimation accuracy of the FoundationPose framework for registering real-world, complex industrial components (e.g., fittings, flanges) from a single RGB-D image without object-specific retraining. In a controlled pre-deployment evaluation with a robot-positioned RGB-D camera, the measured accuracy meets the requirements derived for AR-assisted inspection, achieving median translational deviations between 0.5 mm and 2.8 mm and a median rotational error of approximately 1.9° for plane-symmetric components. While performance remained stable against partial occlusions and reflective surfaces, the results highlight that accuracy is limited by key challenges, including performance degradation with symmetric and texture-poor objects and the need for optimization for real-time mobile deployment. Despite these limitations, the baseline accuracy identified in this study indicates the potential of FoundationPose for industrial AR applications that demand precise alignment of virtual overlays, subject to validation on the target device.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "Christian Masuhr",
      "orcid": "0000-0002-9081-7844"
    },
    {
      "name": "Julian Koch",
      "orcid": "0000-0003-3700-6425"
    },
    {
      "name": "Tim Oels"
    },
    {
      "name": "Thorsten Schüppstuhl",
      "orcid": "0000-0002-9616-3976"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5kd7w93d785kb.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-74702-1",
  "fulltext_url": null,
  "id": "5kd7w93d785kb",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-74702-1",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": "https://www.nature.com/articles/s41598-026-74702-1.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-06",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/5kd7w93d785kb.ris",
  "sources": [
    {
      "first_seen": "2026-11-06T12:20:43Z",
      "native_id": "10.1038/s41598-026-74702-1",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-74702-1"
    }
  ],
  "subjects": [],
  "title": "Evaluating FoundationPose object registration accuracy for AR-guided industrial inspection",
  "title_text": "Evaluating FoundationPose object registration accuracy for AR-guided industrial inspection",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T00:20:56Z",
  "url": "https://research.draconitic.com/paper/5kd7w93d785kb",
  "venue": "Scientific Reports",
  "versions": []
}