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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 the 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-X-4-2024-501-2024</article-id>
<title-group>
<article-title>NeRF-based Localization and Meshing with Wearable Laser Scanning System: A Case Study in Underground Environment</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Zhang</surname>
<given-names>Yizhe</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>Li</surname>
<given-names>Jianping</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>Zhao</surname>
<given-names>Xin</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>Liao</surname>
<given-names>Youqi</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>Dong</surname>
<given-names>Zhen</given-names>
<ext-link>https://orcid.org/0000-0002-0152-3300</ext-link>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Yang</surname>
<given-names>Bisheng</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>LIESMARS, Wuhan University, Wuhan, China</addr-line>
</aff>
<aff id="aff2">
<label>2</label>
<addr-line>Nanyang Technological University, 50 Nanyang Avenue, Singapore</addr-line>
</aff>
<pub-date pub-type="epub">
<day>18</day>
<month>10</month>
<year>2024</year>
</pub-date>
<volume>X-4-2024</volume>
<fpage>501</fpage>
<lpage>506</lpage>
<permissions>
<copyright-statement>Copyright: &#x000a9; 2024 Yizhe Zhang et al.</copyright-statement>
<copyright-year>2024</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/X-4-2024/501/2024/isprs-annals-X-4-2024-501-2024.html">This article is available from https://isprs-annals.copernicus.org/articles/X-4-2024/501/2024/isprs-annals-X-4-2024-501-2024.html</self-uri>
<self-uri xlink:href="https://isprs-annals.copernicus.org/articles/X-4-2024/501/2024/isprs-annals-X-4-2024-501-2024.pdf">The full text article is available as a PDF file from https://isprs-annals.copernicus.org/articles/X-4-2024/501/2024/isprs-annals-X-4-2024-501-2024.pdf</self-uri>
<abstract>
<p>Due to the subterranean scene&amp;rsquo;s poor lighting conditions and variability of the environment, real-time localizing and meshing in complex underground scenes present a challenging task, with high-potential applications in mining and tunnel protection. In this work, we propose a method that combines SLAM and NeRF for mesh reconstruction in the underground environment with a wearable device. First, a LiDAR-Inertial odometry is used for pose estimation. The resulting poses and sequential laser frames are synchronized to generate a novel data structure, scan-block, which is crucial for improving efficiency and ensuring local precision. This structure is designed to enhance efficiency and ensure local precision. Finally, the generated scan-blocks are passed to the backend NeRF for real-time mesh reconstruction. The experiments are carried out in a complex underground space. The experimental results proved that this method achieved good results. The demo video can be found at https://youtu.be/_y1vbaW06EM?si= hFnAl12u-ffU933h.</p>
</abstract>
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