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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-XLVIII-4-W20-2025-39-2026</article-id>
<title-group>
<article-title>Research on the Display of Ultra-Large Point Cloud Data Using a 3DWebGIS Distributed Rendering System</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Kawanabe</surname>
<given-names>Tomohiro</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>Tsuruta</surname>
<given-names>Satoshi</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>Ageno</surname>
<given-names>Minehito</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>Kagatani</surname>
<given-names>Yoshihide</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>Ono</surname>
<given-names>Kenji</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>RIKEN Center for Computational Science, Kobe, Japan</addr-line>
</aff>
<aff id="aff2">
<label>2</label>
<addr-line>GEO Solutions Inc, Nishinomiya, Japan</addr-line>
</aff>
<aff id="aff3">
<label>3</label>
<addr-line>Research Institute for Information Technology, Kyushu University, Fukuoka, Japan</addr-line>
</aff>
<pub-date pub-type="epub">
<day>29</day>
<month>04</month>
<year>2026</year>
</pub-date>
<volume>XLVIII-4/W20-2025</volume>
<fpage>39</fpage>
<lpage>44</lpage>
<permissions>
<copyright-statement>Copyright: &#x000a9; 2026 Tomohiro Kawanabe et al.</copyright-statement>
<copyright-year>2026</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/XLVIII-4-W20-2025/39/2026/isprs-archives-XLVIII-4-W20-2025-39-2026.html">This article is available from https://isprs-archives.copernicus.org/articles/XLVIII-4-W20-2025/39/2026/isprs-archives-XLVIII-4-W20-2025-39-2026.html</self-uri>
<self-uri xlink:href="https://isprs-archives.copernicus.org/articles/XLVIII-4-W20-2025/39/2026/isprs-archives-XLVIII-4-W20-2025-39-2026.pdf">The full text article is available as a PDF file from https://isprs-archives.copernicus.org/articles/XLVIII-4-W20-2025/39/2026/isprs-archives-XLVIII-4-W20-2025-39-2026.pdf</self-uri>
<abstract>
<p>This paper presents a method for ultra-high-resolution visualization of large-scale 3DWebGIS data using ChOWDER, a web-based scalable display system, combined with the open-source 3DWebGIS application iTowns. Web browsers impose a heap memory limit of about 4 GB, which restricts conventional WebGIS from handling very large 3D datasets. ChOWDER distributes 3DWebGIS rendering across multiple browser-based display clients, expanding both memory space and screen space. As a case study, we convert cloud data from the Himawari meteorological satellite into approximately 500 million 3D points, generate 3DTiles (about 6.1 GB, eight-level octree), and render them on a tiled display wall composed of fifteen 4K displays (20K horizontal resolution). Each display browser renders only its portion of the scene, enabling detailed inspection of cloud structures while preserving an overview.&lt;/p&gt;
&lt;p&gt;During visualization, we observe triangular non-rendered regions in the point cloud. Analysis shows that these artifacts arise from the use of an Earth-centered Cartesian coordinate system (EPSG:4978), which causes 3DTiles bounding voxels to intersect the Earth&amp;rsquo;s surface, combined with WebGIS behavior that occasionally fails to display tiles at the requested zoom level. We argue that such issues are inherent when mapping global-scale 3D data onto WebGIS platforms. As a mitigation strategy, we propose presegmenting global data into multiple regions and generating 3DTiles separately so that bounding volumes do not span the Earth&amp;rsquo;s surface. Future work includes implementing and validating this region-segmented workflow, and comparing 3DTiles with alternative point-cloud formats such as Potree and COPC for performance and artifact behavior.</p>
</abstract>
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