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<article article-type="research-article">
  <front>
    <journal-meta>
      <journal-id journal-id-type="aggregator">72010604</journal-id>
      <journal-title>Electronic Imaging</journal-title>
      <issn pub-type="ppub">2470-1173</issn><issn pub-type="epub"></issn>
      <publisher>
        <publisher-name>Society for Imaging Science and Technology</publisher-name>
        <publisher-loc>IS&amp;T 7003 Kilworth Lane, Springfield, VA 22151 USA</publisher-loc>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.2352/ISSN.2470-1173.2021.5.MAAP-139</article-id>
      <article-id pub-id-type="sici">2470-1173(20210118)2021:5L.1391;1-</article-id>
      <article-id pub-id-type="publisher-id">ei_24701173_v2021n5_Input/s5.xml</article-id>
      <article-id pub-id-type="other">/ist/ei/2021/00002021/00000005/art00005</article-id>
      <article-categories>
        <subj-group>
          <subject>Articles</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Surface roughness estimation using structured light projection</article-title>
      </title-group>
      <contrib-group>
        <contrib>
          <name>
            <surname>Shahpaski</surname>
            <given-names>Marjan</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Sapaico</surname>
            <given-names>Luis Ricardo</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Süsstrunk</surname>
            <given-names>Sabine</given-names>
          </name>
        </contrib>
      </contrib-group>
      <pub-date>
        <day>18</day>
        <month>01</month>
        <year>2021</year>
      </pub-date>
      <volume>2021</volume>
      <issue>5</issue>
      <fpage>139-1</fpage>
      <lpage>139-7</lpage>
      <permissions>
        <copyright-year>2021</copyright-year>
      </permissions>
      <abstract>
        <p>
          <italic>How we visually perceive non-emissive objects in our surrounding depends on the interaction of light with the optical characteristics of the materials that comprise them. The macroscopic surface roughness can also influence the appearance through shadowing and interreflections. In
 this work, we use a structured light scanner to estimate the surface structure of near-planar surfaces, namely of printing textiles. We compare our scans, both qualitatively and quantitatively, to those from a commercial highgrade profilometer based on the confocal principle. We achieve comparable
 results to the profilometer on samples with moderately complex surfaces. We discuss the possible reasons for errors in the scans of complex surfaces, thus providing guidelines for robust depth estimation. This comparison can help other researchers build more robust acquisition setups by understanding
 and minimizing the errors inherent to the reconstruction methods.</italic>
        </p>
      </abstract>
      <kwd-group>
        <kwd>Structured light</kwd>
        <kwd>Depth estimation</kwd>
        <kwd>Surface roughness</kwd>
        <kwd>Optical profilometry</kwd>
        <kwd>2.5D reconstruction</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
