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<front>
<journal-meta>
<journal-id journal-id-type="publisher">ISPRS-Archives</journal-id>
<journal-title-group>
<journal-title>ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences</journal-title>
<abbrev-journal-title abbrev-type="publisher">ISPRS-Archives</abbrev-journal-title>
<abbrev-journal-title abbrev-type="nlm-ta">Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">2194-9034</issn>
<publisher><publisher-name>Copernicus Publications</publisher-name>
<publisher-loc>Göttingen, Germany</publisher-loc>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.5194/isprs-archives-XLII-2-W13-849-2019</article-id>
<title-group>
<article-title>IMPROVING AUTOMATIC RECONSTRUCTION OF INTERIOR WALLS FROM POINT CLOUD DATA</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Maset</surname>
<given-names>E.</given-names>

</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<ext-link>https://orcid.org/0000-0003-3689-1960</ext-link></contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Magri</surname>
<given-names>L.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Fusiello</surname>
<given-names>A.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>DPIA – University of Udine, Via delle Scienze, 206 – 33100 Udine, Italy</addr-line>
</aff>
<pub-date pub-type="epub">
<day>05</day>
<month>06</month>
<year>2019</year>
</pub-date>
<volume>XLII-2/W13</volume>
<fpage>849</fpage>
<lpage>855</lpage>
<permissions>
<copyright-statement>Copyright: © 2019 E. Maset et al.</copyright-statement>
<copyright-year>2019</copyright-year>
<license license-type="open-access">
<license-p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link></license-p>
</license>
</permissions>
<self-uri xlink:href="https://isprs-archives.copernicus.org/articles/isprs-archives-XLII-2-W13-849-2019.html">This article is available from https://isprs-archives.copernicus.org/articles/isprs-archives-XLII-2-W13-849-2019.html</self-uri>
<self-uri xlink:href="https://isprs-archives.copernicus.org/articles/isprs-archives-XLII-2-W13-849-2019.pdf">The full text article is available as a PDF file from https://isprs-archives.copernicus.org/articles/isprs-archives-XLII-2-W13-849-2019.pdf</self-uri>
<abstract>
<p>In this paper we deal with the automatic reconstruction of interior walls from point clouds, an active research topic with several practical applications. We propose an improved version of the method presented in (Magri and Fusiello, 2018), where the overall structure of the environment is extracted by fitting lines to the main building features, using Min-hashed J-Linkage as a multi-model fitting technique. Our variation has the merit of producing more accurate results, both in terms of wall reconstruction and room segmentation, and greatly reducing the need for user-defined thresholds.</p>
</abstract>
<counts><page-count count="7"/></counts>
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