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<front>
<journal-meta>
<journal-id journal-id-type="publisher">ISPRS-Archives</journal-id>
<journal-title-group>
<journal-title>The 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-XLIII-B5-2020-207-2020</article-id>
<title-group>
<article-title>THE ISPRS BENCHMARK ON INDOOR MODELLING – PRELIMINARY RESULTS</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Khoshelham</surname>
<given-names>K.</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>Tran</surname>
<given-names>H.</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>Acharya</surname>
<given-names>D.</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>Díaz Vilariño</surname>
<given-names>L.</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Kang</surname>
<given-names>Z.</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Dalyot</surname>
<given-names>S.</given-names>
</name>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>Dept. of Infrastructure Engineering, The University of Melbourne, Parkville 3010 Australia</addr-line>
</aff>
<aff id="aff2">
<label>2</label>
<addr-line>Applied Geotechnologies Group, School of Industrial Engineering, University of Vigo, Spain</addr-line>
</aff>
<aff id="aff3">
<label>3</label>
<addr-line>Dept. of Remote Sensing and Geo-Information Engineering, School of Land Science and Technology, China University of Geosciences, Beijing 100083, China</addr-line>
</aff>
<aff id="aff4">
<label>4</label>
<addr-line>Environmental Crowdsourcing Laboratory, Faculty of Civil and Environmental Engineering, Technion - Israel Institute of Technology, Haifa 3200003, Israel</addr-line>
</aff>
<pub-date pub-type="epub">
<day>24</day>
<month>08</month>
<year>2020</year>
</pub-date>
<volume>XLIII-B5-2020</volume>
<fpage>207</fpage>
<lpage>211</lpage>
<permissions>
<copyright-statement>Copyright: &#x000a9; 2020 K. Khoshelham et al.</copyright-statement>
<copyright-year>2020</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/XLIII-B5-2020/207/2020/isprs-archives-XLIII-B5-2020-207-2020.html">This article is available from https://isprs-archives.copernicus.org/articles/XLIII-B5-2020/207/2020/isprs-archives-XLIII-B5-2020-207-2020.html</self-uri>
<self-uri xlink:href="https://isprs-archives.copernicus.org/articles/XLIII-B5-2020/207/2020/isprs-archives-XLIII-B5-2020-207-2020.pdf">The full text article is available as a PDF file from https://isprs-archives.copernicus.org/articles/XLIII-B5-2020/207/2020/isprs-archives-XLIII-B5-2020-207-2020.pdf</self-uri>
<abstract>
<p>Automated 3D reconstruction of indoor environments from point clouds has been a topic of intensive research in recent years. Different methods developed for the generation of 3D indoor models have achieved promising results on different case studies. However, a comprehensive evaluation and comparison of the performance of these methods has not been available. This paper presents the preliminary results of the ISPRS benchmark on indoor modelling, an initiative of Working Group IV/5 to benchmark the performance of indoor modelling methods using a public dataset and a comprehensive evaluation framework. The performances of the different methods are compared through geometric quality evaluation of the reconstructed models in terms of completeness, correctness, and accuracy of wall elements. The results show that the reconstruction methods generally achieve high completeness but lower correctness for the reconstructed models while accuracies range from 0.5 cm to 6.7 cm.</p>
</abstract>
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