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<front>
<journal-meta>
<journal-id journal-id-type="publisher">ISPRS-Annals</journal-id>
<journal-title-group>
<journal-title>ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences</journal-title>
<abbrev-journal-title abbrev-type="publisher">ISPRS-Annals</abbrev-journal-title>
<abbrev-journal-title abbrev-type="nlm-ta">ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">2194-9050</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-annals-IV-2-W7-235-2019</article-id>
<title-group>
<article-title>DIRECT CO-REGISTRATION OF TIR IMAGES AND MLS POINT CLOUDS BY CORRESPONDING KEYPOINTS</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Zhu</surname>
<given-names>J.</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>Xu</surname>
<given-names>Y.</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>Hoegner</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>Stilla</surname>
<given-names>U.</given-names>

</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<ext-link>https://orcid.org/0000-0002-1184-0924</ext-link></contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>Photogrammetry and Remote Sensing, Technical University of Munich (TUM), Germany</addr-line>
</aff>
<pub-date pub-type="epub">
<day>16</day>
<month>09</month>
<year>2019</year>
</pub-date>
<volume>IV-2/W7</volume>
<fpage>235</fpage>
<lpage>242</lpage>
<permissions>
<copyright-statement>Copyright: © 2019 J. Zhu 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-annals.copernicus.org/articles/isprs-annals-IV-2-W7-235-2019.html">This article is available from https://isprs-annals.copernicus.org/articles/isprs-annals-IV-2-W7-235-2019.html</self-uri>
<self-uri xlink:href="https://isprs-annals.copernicus.org/articles/isprs-annals-IV-2-W7-235-2019.pdf">The full text article is available as a PDF file from https://isprs-annals.copernicus.org/articles/isprs-annals-IV-2-W7-235-2019.pdf</self-uri>
<abstract>
<p>In this work, we discussed how to directly combine thermal infrared image (TIR) and the point cloud without additional assistance from GCPs or 3D models. Specifically, we propose a point-based co-registration process for combining the TIR image and the point cloud for the buildings. The keypoints are extracted from images and point clouds via primitive segmentation and corner detection, then pairs of corresponding points are identified manually. After that, the estimated camera pose can be computed with EPnP algorithm. Finally, the point cloud with thermal information provided by IR images can be generated as a result, which is helpful in the tasks such as energy inspection, leakage detection, and abnormal condition monitoring. This paper provides us more insight about the probability and ideas about the combining TIR image and point cloud.</p>
</abstract>
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