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      <title>
    <![CDATA[ LumiDB blog ]]>
  </title>
  <description>
    <![CDATA[ We publish case studies, product updates, and technical guidance on handling large-scale 3D data. Our goal is to show how organizations use LumiDB to simplify point-cloud workflows, improve performance, and modernize their spatial data infrastructure. ]]>
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  <link>https://blog.lumidb.com/</link>
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      <item>
        <title>
          <![CDATA[ Layers: getting the project data organized ]]>
        </title>
        <description>
          <![CDATA[ The viewer's sidebar is now a proper layers panel. Reorder, group, and lock every dataset in a project, show several rasters at once, and manage your measurements as layers alongside the point cloud — a first step toward handling survey CAD. ]]>
        </description>
        <link>https://blog.lumidb.com/layers</link>
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        <category>
        <![CDATA[ Viewer ]]>
      </category>  
          <dc:creator>
          <![CDATA[ Sampo Lappalainen ]]>
        </dc:creator>
        
        <pubDate>2026-06-25T00:00:00.000Z</pubDate>
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      <item>
        <title>
          <![CDATA[ LumiDB and Flai partner for next-gen point cloud workflows ]]>
        </title>
        <description>
          <![CDATA[ LumiDB is officially partnering with Flai, a leader in building advanced AI models for automated LiDAR point cloud classification. This partnership directly advances LumiDB’s core mission: making 3D point cloud data instantly usable for every team that needs it. ]]>
        </description>
        <link>https://blog.lumidb.com/lumidb-x-flai</link>
        <guid isPermaLink="false">36711b43-120a-8031-b55d-f16a1e6856c3</guid>
          
          <dc:creator>
          <![CDATA[ Xabier Erana ]]>
        </dc:creator>
        
        <pubDate>2026-06-17T00:00:00.000Z</pubDate>
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      </item>
      
      <item>
        <title>
          <![CDATA[ LumiDB now supports orthophotos ]]>
        </title>
        <description>
          <![CDATA[ Orthophoto support is now live in LumiDB. You can ingest a multi-file GeoTIFF dataset with sidecar files, preview it in the Viewer and connect it to downstream tools via WMTS or XYZ endpoints. This opens up interesting “cross-pollination” opportunities, such as coloring point clouds with orthophoto imagery or computing elevation-based analytics. ]]>
        </description>
        <link>https://blog.lumidb.com/lumidb-orthophotos</link>
        <guid isPermaLink="false">36c11b43-120a-806c-b04a-d50c95f12ada</guid>
        <category>
        <![CDATA[ API ]]>
      </category><category>
        <![CDATA[ Viewer ]]>
      </category>  
          <dc:creator>
          <![CDATA[ Jasin Bushnaief ]]>
        </dc:creator>
        
        <pubDate>2026-05-26T00:00:00.000Z</pubDate>
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      </item>
      
      <item>
        <title>
          <![CDATA[ Esri ArcGIS Integration is Finally Here! ]]>
        </title>
        <description>
          <![CDATA[ LumiDB is now integrated with ArcGIS. 3D point cloud data in LumiDB streams directly into ArcGIS with no file exports or format conversions in between. ]]>
        </description>
        <link>https://blog.lumidb.com/lumidb-arcgis</link>
        <guid isPermaLink="false">33c11b43-120a-803a-bdc3-fd358a8e925f</guid>
          
          <dc:creator>
          <![CDATA[ Jasin Bushnaief ]]>
        </dc:creator>
        
        <pubDate>2026-04-08T00:00:00.000Z</pubDate>
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      </item>
      
      <item>
        <title>
          <![CDATA[ LumiDB and Pointly announce partnership to connect complex spatial data workflows ]]>
        </title>
        <description>
          <![CDATA[ We are partnering with Pointly, a Berlin-based company specializing in AI-driven 3D point cloud classification, to build a direct connection between our two platforms. The goal is to make the combined workflow easier for the organizations that already use both tools. ]]>
        </description>
        <link>https://blog.lumidb.com/lumidb-pointly</link>
        <guid isPermaLink="false">32011b43-120a-8044-af70-f28110ecb801</guid>
          
          <dc:creator>
          <![CDATA[ Xabier Erana ]]>
        </dc:creator>
        
        <pubDate>2026-03-13T00:00:00.000Z</pubDate>
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      </item>
      
      <item>
        <title>
          <![CDATA[ TerraScan Introduces Direct Connection to LumiDB  ]]>
        </title>
        <description>
          <![CDATA[ For years, the LiDAR industry has been defined by fragmented workflows. LumiDB was built to change that; it was designed to be the central bridge for the entire point cloud lifecycle. With the release of TerraScan version 026.006 (March 2, 2026), TerraScan is now natively integrated with LumiDB. By establishing this direct link, we are laying the groundwork for what comes next. ]]>
        </description>
        <link>https://blog.lumidb.com/lumidb-x-terrascan</link>
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          <dc:creator>
          <![CDATA[ Xabier Erana ]]>
        </dc:creator>
        
        <pubDate>2026-03-06T00:00:00.000Z</pubDate>
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      </item>
      
      <item>
        <title>
          <![CDATA[ Volume Measurement Using LumiDB ]]>
        </title>
        <description>
          <![CDATA[ One of the most common feature requests we get is volumetric measurement. When you have 3D point clouds, it’s quite important to be able to measure, say, the volume of a stockpile or an excavated pit. This feature is now coming to LumiDB, and it’s really easy: you just draw a polygon around the pile or pit of interest, and LumiDB will show its volume. ]]>
        </description>
        <link>https://blog.lumidb.com/volume-measurement-using-lumidb</link>
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          <dc:creator>
          <![CDATA[ Jasin Bushnaief ]]>
        </dc:creator>
        
        <pubDate>2026-02-11T00:00:00.000Z</pubDate>
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      </item>
      
      <item>
        <title>
          <![CDATA[ 1.7 Trillion Points: Ingesting the Entire North Island into LumiDB ]]>
        </title>
        <description>
          <![CDATA[ We ingested 1.73 trillion Lidar points covering New Zealand’s North Island, transforming 13 TB of data and put it behind a seamless, queryable API. By moving away from traditional workflows that rely on 300,000 fragmented "tiles," this country-scale dataset provides a single source of truth that significantly reduces time to insight and enables instant, interactive visualization directly in the browser. Whether you are querying a single building or a 700 km cross-section, the system eliminates file boundaries to ensure that managing a massive archive feels as lightweight and accessible as a million-point scan. ]]>
        </description>
        <link>https://blog.lumidb.com/ingesting-the-north-island</link>
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        <category>
        <![CDATA[ API ]]>
      </category>  
          <dc:creator>
          <![CDATA[ Jasin Bushnaief ]]>
        </dc:creator>
        
        <pubDate>2026-01-22T00:00:00.000Z</pubDate>
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      </item>
      
      <item>
        <title>
          <![CDATA[ How Forum Virium Helsinki enabled the City of Helsinki to modernize reality capture ]]>
        </title>
        <description>
          <![CDATA[ Forum Virium Helsinki partnered with LumiDB to facilitate a technology pilot for the City of Helsinki’s Urban Environment Division. The initiative replaced manual hard drive shipments with API-driven data streaming, demonstrating how a centralized database improves security and accessibility for city planners. ]]>
        </description>
        <link>https://blog.lumidb.com/case-study-forum-virium</link>
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        <category>
        <![CDATA[ Case study ]]>
      </category>  
          
        
        <pubDate>2025-12-17T00:00:00.000Z</pubDate>
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      </item>
      
      <item>
        <title>
          <![CDATA[ How Infrakit uses LumiDB to turn reality-capture data into actionable field measurements ]]>
        </title>
        <description>
          <![CDATA[ Infrakit partnered with LumiDB to turn drone and LiDAR scans into actionable field measurements. By replacing file-based workflows with a database built for large point clouds, the team can complete depth and distance checks directly from the office. The approach is at least 50% faster than traditional site-based methods and produces more consistent documentation for regulatory requirements. ]]>
        </description>
        <link>https://blog.lumidb.com/case-study-infrakit-2</link>
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        <category>
        <![CDATA[ Case study ]]>
      </category>  
          <dc:creator>
          <![CDATA[ Sampo Lappalainen ]]>
        </dc:creator>
        
        <pubDate>2025-12-03T00:00:00.000Z</pubDate>
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      </item>
      
      <item>
        <title>
          <![CDATA[ LumiDB ❤️ QGIS ]]>
        </title>
        <description>
          <![CDATA[ We have added support for streaming point clouds directly from LumiDB into QGIS, allowing you to use your data for map creation or geospatial analysis without downloading massive files. By utilizing EPT links, users can instantly stream datasets with full Level of Detail support. This feature takes advantage of LumiDB’s runtime transformation, meaning all existing data is ready to use immediately without time-consuming re-indexing or pre-processing. ]]>
        </description>
        <link>https://blog.lumidb.com/lumidb-heart-qgis</link>
        <guid isPermaLink="false">2b711b43-120a-80c5-b67f-fb73ba0918d0</guid>
        <category>
        <![CDATA[ API ]]>
      </category>  
          <dc:creator>
          <![CDATA[ Jasin Bushnaief ]]>
        </dc:creator>
        
        <pubDate>2025-11-27T00:00:00.000Z</pubDate>
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      </item>
      
      <item>
        <title>
          <![CDATA[ How Helsinki’s GIS Centre expanded access to 3D city data with LumiDB ]]>
        </title>
        <description>
          <![CDATA[ The City of Helsinki’s GIS Centre partnered with LumiDB to test whether its growing archive of point-cloud data could be made broadly usable through browser-based access. The pilot showed faster loading, simpler workflows, and reduced dependency on specialist desktop tools. This case study outlines the problem, the approach, and what changed for planners and GIS teams. ]]>
        </description>
        <link>https://blog.lumidb.com/case-study-helsinki</link>
        <guid isPermaLink="false">2b611b43-120a-803a-9692-cb910f07e83c</guid>
        <category>
        <![CDATA[ Case study ]]>
      </category>  
          <dc:creator>
          <![CDATA[ Sampo Lappalainen ]]>
        </dc:creator>
        
        <pubDate>2025-11-26T00:00:00.000Z</pubDate>
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      </item>
      
      <item>
        <title>
          <![CDATA[ How Infrakit simplified large-scale point-cloud visualization with LumiDB’s query-driven engine ]]>
        </title>
        <description>
          <![CDATA[ Infrakit partnered with LumiDB to streamline its point-cloud workflows and replace file-based LOD pipeline. The integration delivered faster rendering, sub-second cross-sections, and a simpler, more stable architecture for handling large LiDAR and drone datasets. This case study outlines how the change was implemented and what improved for engineers and end users. ]]>
        </description>
        <link>https://blog.lumidb.com/case-study-infrakit</link>
        <guid isPermaLink="false">2ae11b43-120a-8084-81d0-ebcf041cc1c8</guid>
        <category>
        <![CDATA[ Case study ]]>
      </category>  
          <dc:creator>
          <![CDATA[ Sampo Lappalainen ]]>
        </dc:creator>
        
        <pubDate>2025-11-18T00:00:00.000Z</pubDate>
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      </item>
      
      <item>
        <title>
          <![CDATA[ Swipe Compare: Easy way to inspect differences between scans ]]>
        </title>
        <description>
          <![CDATA[ Swipe Compare: visually slide between two scans. See changes in construction, vegetation, or infrastructure with aligned point clouds. ]]>
        </description>
        <link>https://blog.lumidb.com/swipe-compare-easy-way-to-inspect-differences-between-scans</link>
        <guid isPermaLink="false">23211b43-120a-80f5-bf3c-cf38e3b8d9d5</guid>
        <category>
        <![CDATA[ Viewer ]]>
      </category>  
          <dc:creator>
          <![CDATA[ Sampo Lappalainen ]]>
        </dc:creator>
        
        <pubDate>2025-07-16T00:00:00.000Z</pubDate>
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      </item>
      
      <item>
        <title>
          <![CDATA[ A C/C++ API for data ingestion ]]>
        </title>
        <description>
          <![CDATA[ We’ve released a C client library for importing data into LumiDB. It wraps our HTTP API with a simple C interface. Ideal for integrating with existing C or C++ systems. ]]>
        </description>
        <link>https://blog.lumidb.com/a-c/c-api-for-data-ingestion</link>
        <guid isPermaLink="false">23111b43-120a-8015-9c55-d303281bfb3c</guid>
        <category>
        <![CDATA[ API ]]>
      </category>  
          <dc:creator>
          <![CDATA[ Alex Lagerstedt ]]>
        </dc:creator>
        
        <pubDate>2025-07-15T00:00:00.000Z</pubDate>
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      </item>
      
      <item>
        <title>
          <![CDATA[ New in LumiDB: Progressive LOD Streaming Viewer ]]>
        </title>
        <description>
          <![CDATA[ We’ve brought progressive 3D Tiles streaming to the LumiDB Viewer, letting users instantly see full 3D datasets and get live query results without waiting. This major upgrade is part of a broader summer rollout focused on delivering a smooth, high-performance experience for exploring and analyzing massive point cloud datasets.
 ]]>
        </description>
        <link>https://blog.lumidb.com/progressive-lod-streaming-viewer</link>
        <guid isPermaLink="false">21f11b43-120a-8089-8d35-d1a15a3f3126</guid>
        <category>
        <![CDATA[ Viewer ]]>
      </category>  
          <dc:creator>
          <![CDATA[ Jasin Bushnaief ]]>
        </dc:creator>
        
        <pubDate>2025-06-27T00:00:00.000Z</pubDate>
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      </item>
      
      <item>
        <title>
          <![CDATA[ Managing infrastructure-scale point cloud data with LumiDB ]]>
        </title>
        <description>
          <![CDATA[ National infrastructure operators are generating massive volumes of 3D scan data from drones, mobile scanners, and contractors. This post outlines how LumiDB helps integrate point clouds into GIS, CAD, and BIM workflows as part of a unified digital twin strategy. ]]>
        </description>
        <link>https://blog.lumidb.com/managing-infrastructure-scale-point-cloud-data-with-lumidb</link>
        <guid isPermaLink="false">20f11b43-120a-80b9-9a2e-d6b089264395</guid>
          
          <dc:creator>
          <![CDATA[ Sampo Lappalainen ]]>
        </dc:creator>
        
        <pubDate>2025-06-11T00:00:00.000Z</pubDate>
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      </item>
      
      <item>
        <title>
          <![CDATA[ 3D Tiles Support in LumiDB: Adaptive Streaming for Any Application ]]>
        </title>
        <description>
          <![CDATA[ LumiDB now supports adaptive level-of-detail streaming via 3D Tiles. This means easy integration into any Cesium.js-based tool, or any tool requiring adaptive streaming of reality capture datasets, even massive ones. We’re also bringing LOD streaming into our own viewer app. ]]>
        </description>
        <link>https://blog.lumidb.com/3d-tiles-support-in-lumidb</link>
        <guid isPermaLink="false">20d11b43-120a-8095-a005-f10da002882f</guid>
        <category>
        <![CDATA[ API ]]>
      </category>  
          <dc:creator>
          <![CDATA[ Jasin Bushnaief ]]>
        </dc:creator>
        
        <pubDate>2025-06-09T00:00:00.000Z</pubDate>
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      </item>
      
      <item>
        <title>
          <![CDATA[ Step-by-Step: Importing Files into LumiDB Using the API ]]>
        </title>
        <description>
          <![CDATA[ This post shows you how to import reality capture files into a LumiDB table, by walking through our simple import API. ]]>
        </description>
        <link>https://blog.lumidb.com/lumidb-import-api-tutorial</link>
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        <category>
        <![CDATA[ API ]]>
      </category>  
          <dc:creator>
          <![CDATA[ Jasin Bushnaief ]]>
        </dc:creator>
        
        <pubDate>2025-04-10T00:00:00.000Z</pubDate>
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      </item>
      
      <item>
        <title>
          <![CDATA[ Drowning in Reality Capture Data? Why Traditional Storage is Failing You ]]>
        </title>
        <description>
          <![CDATA[ Reality capture data is a powerful asset, until it turns into a liability. Scans pile up across cloud drives and servers, buried in endless folders with no clear way to retrieve, compare, or integrate them. Teams waste hours chasing down the right version, manually stitching together datasets, and fighting with outdated storage systems. It’s an expensive mess. But what if data didn’t have to be scattered and frustrating? What if it were instantly accessible, easy to explore, and seamlessly connected to your workflows?
 ]]>
        </description>
        <link>https://blog.lumidb.com/drowning-in-reality-capture-data-why-traditional-storage-is-failing-you</link>
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          <dc:creator>
          <![CDATA[ Sampo Lappalainen ]]>
        </dc:creator>
        
        <pubDate>2025-03-10T00:00:00.000Z</pubDate>
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          <![CDATA[ Importing a Quarter Trillion Points ]]>
        </title>
        <description>
          <![CDATA[ Managing and using a terabyte-scale point cloud dataset becomes painful when working with traditional file-based tools and methods. LumiDB might be able to help you here! In this case study we investigate how such a dataset is imported into LumiDB. ]]>
        </description>
        <link>https://blog.lumidb.com/importing-a-quarter-trillion-points</link>
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        <![CDATA[ API ]]>
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          <dc:creator>
          <![CDATA[ Alex Lagerstedt ]]>
        </dc:creator>
        
        <pubDate>2025-02-10T00:00:00.000Z</pubDate>
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      <item>
        <title>
          <![CDATA[ Leveraging Metadata in LumiDB Queries: A Multi-Scanner Example ]]>
        </title>
        <description>
          <![CDATA[ Managing large 3D scan datasets efficiently is challenging—especially when dealing with strict memory constraints. In this post, we explore how metadata queries in LumiDB let you interactively enable and disable scans without ever loading the full dataset into memory. We’ll walk through a real-world example, where a building scan is split into multiple scanner positions, and show how LumiDB’s built-in filtering and level-of-detail (LOD) handling can keep your application fast and responsive. 🚀 ]]>
        </description>
        <link>https://blog.lumidb.com/leveraging-metadata-in-lumidb-queries-a-multi-scanner-example</link>
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        <![CDATA[ API ]]>
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          <dc:creator>
          <![CDATA[ Jasin Bushnaief ]]>
        </dc:creator>
        
        <pubDate>2025-02-03T00:00:00.000Z</pubDate>
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        <title>
          <![CDATA[ Visualizing Massive 3D Point Clouds with Dynamic Level-of-Detail ]]>
        </title>
        <description>
          <![CDATA[ Visualizing large 3D point cloud datasets can be a daunting task. With LumiDB, users store their data in a special purpose database that enables efficient querying based on point budget or density, eliminating the need for preprocessing. Beyond visualization, the stored points remain fully usable for other workflows. This post explores the challenges of visualizing massive point cloud datasets and how LumiDB helps. ]]>
        </description>
        <link>https://blog.lumidb.com/visualizing-massive-3d-point-clouds-with-dynamic-level-of-detail</link>
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          <dc:creator>
          <![CDATA[ Sampo Lappalainen ]]>
        </dc:creator>
        
        <pubDate>2025-01-22T00:00:00.000Z</pubDate>
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          <![CDATA[ LumiDB: The Simplest Possible API for Reality Capture Data ]]>
        </title>
        <description>
          <![CDATA[ We’re releasing our minimal-yet-powerful runtime API. Whether you’re visualizing in Three.js or exporting data to 3rd party tools, LumiDB is built to make managing reality capture data easy and efficient. ]]>
        </description>
        <link>https://blog.lumidb.com/lumidb-the-simplest-possible-api-to-query-reality-capture-data</link>
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        <![CDATA[ API ]]>
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          <dc:creator>
          <![CDATA[ Jasin Bushnaief ]]>
        </dc:creator>
        
        <pubDate>2024-12-11T00:00:00.000Z</pubDate>
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          <![CDATA[ File Chaos: Why the Construction Industry Needs a New Approach to Managing Reality Capture Data ]]>
        </title>
        <description>
          <![CDATA[ Reality capture data is growing exponentially in the construction industry, yet the methods for managing it remain outdated and inefficient. At LumiDB, we’re building a solution to tackle this problem, paving the way for a future where construction sites are fully integrated and autonomous. ]]>
        </description>
        <link>https://blog.lumidb.com/file-chaos-why-the-construction-industry-needs-a-new-approach-to-managing-reality-capture-data</link>
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          <dc:creator>
          <![CDATA[ Sampo Lappalainen ]]>
        </dc:creator>
        
        <pubDate>2024-11-26T00:00:00.000Z</pubDate>
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        <title>
          <![CDATA[ Why We’re Fixing Reality Capture Data ]]>
        </title>
        <description>
          <![CDATA[ From hacking together data management software for autonomous robots at Amazon to starting LumiDB, this is the story of how we set out to fix reality capture data. Learn how we’re tackling the challenges of exploding data volumes, outdated tools, and scattered workflows to build a future where reality capture data is easily accessible. ]]>
        </description>
        <link>https://blog.lumidb.com/why-we-re-fixing-reality-capture-data</link>
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        <category>
        <![CDATA[ LumiDB ]]>
      </category>  
          <dc:creator>
          <![CDATA[ Sampo Lappalainen ]]>
        </dc:creator><dc:creator>
          <![CDATA[ Jasin Bushnaief ]]>
        </dc:creator>
        
        <pubDate>2024-11-15T00:00:00.000Z</pubDate>
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