Device for Digitizing Materials: Defensive-Publication Embodiments, Version 2026-05-16

This record contains a defensive publication describing material-scanning devices and methods, including photometric stereo, SVBRDF/BRDF capture, polarized reflection and transmission capture, spectral illumination, material-map generation, renderable material output, calibration, machine-learning-assisted reconstruction, quality control, and related embodiments. This version is an expanded defensive-publication version prepared from an initial document first published on 2023-01-02. Earlier signed versions are preserved separately and referenced for provenance. The expanded 2026 version should not be interpreted as having been publicly available before its stated publication date.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-16
DOI
https://doi.org/10.5281/zenodo.20229293
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
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article

Device for Digitizing Materials: Defensive-Publication Embodiments, Version 2026-05-16

Niklas Hauber
Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Materials Science
article

Device for Digitizing Materials: Defensive-Publication Embodiments, Version 2026-05-16

Niklas Hauber
article en

Abstract

This record contains a defensive publication describing material-scanning devices and methods, including photometric stereo, SVBRDF/BRDF capture, polarized reflection and transmission capture, spectral illumination, material-map generation, renderable material output, calibration, machine-learning-assisted reconstruction, quality control, and related embodiments. This version is an expanded defensive-publication version prepared from an initial document first published on 2023-01-02. Earlier signed versions are preserved separately and referenced for provenance. The expanded 2026 version should not be interpreted as having been publicly available before its stated publication date.

Zenodo (CERN European Organization for Nuclear Research)
Sustainable cities and communities
Openalex Percentile: Top 10%
Machine Learning in Materials Science
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