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#pointcloud — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #pointcloud, aggregated by home.social.

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  1. Vandaag staan Raymond Nijssen & ik op de HAS #GeoExperience in Den Bosch met een #QGIS stand. We geven ook een workshop over #pointcloud processing in QGIS. Niet erbij? De workshop is gratis online te volgen op @[email protected]: courses.gisopencourseware.org/course/view.... @[email protected]

  2. Vandaag staan @terglobo en ik op de HAS in 's-Hertogenbosch met een ‑stand. We geven ook een workshop over processing & in QGIS.

    Niet erbij? De workshop is gratis online te volgen op @gisocw: courses.gisopencourseware.org/

    Er staan nóg meer NL‑cursussen op GIS OpenCourseWare. Zie: courses.gisopencourseware.org/

    @qgisnl

  3. Vandaag staan @terglobo en ik op de HAS #GeoExperience in 's-Hertogenbosch met een #QGIS‑stand. We geven ook een workshop over #pointcloud processing & #3D #visualisatie in QGIS.

    Niet erbij? De workshop is gratis online te volgen op @gisocw: courses.gisopencourseware.org/

    Er staan nóg meer NL‑cursussen op GIS OpenCourseWare. Zie: courses.gisopencourseware.org/

    @qgisnl

  4. During my studies, I connected with the company that captured a point cloud of the iconic Agfa-Gevaert factory nearby. It’s fascinating to see these two technologies converge in a single file.
    more info: jeroenbocken.com/works/3d_lase

    #DigitalArt #Factory #Pointcloud

  5. TIL that there are 360° LIDAR scanners below $450. With open source programs. I desperately need to figure out why I do need one.

    #osm #indoormapping #pointcloud

  6. TIL that there are 360° LIDAR scanners below $450. With open source programs. I desperately need to figure out why I do need one.

    #osm #indoormapping #pointcloud

  7. @geospacedman giro3d.org for #3D Geospatial visualization, not only #PointCloud . Are you looking for specific types of dataset / examples ?

  8. @geospacedman giro3d.org for #3D Geospatial visualization, not only #PointCloud . Are you looking for specific types of dataset / examples ?

  9. I was running out of RAM and out of swap while trying to generate a copc out of a laz with #pdal (the laz weighs 4.7GB). So I created a 15GB swapfile by simply adding

    swapDevices = [{
    device = "/var/lib/swapfile_15G";
    size = 15*1024;
    }];

    to configuration.nix, rebuild, and voilà. The process is currently running very smoothly, peak consuming the whole RAM as expected (64GB) + 12GB out of the available 24GB.

    cc @sguimmara @autra

    #linux #nixos #pointcloud #lidar

  10. I was running out of RAM and out of swap while trying to generate a copc out of a laz with #pdal (the laz weighs 4.7GB). So I created a 15GB swapfile by simply adding

    swapDevices = [{
    device = "/var/lib/swapfile_15G";
    size = 15*1024;
    }];

    to configuration.nix, rebuild, and voilà. The process is currently running very smoothly, peak consuming the whole RAM as expected (64GB) + 12GB out of the available 24GB.

    cc @sguimmara @autra

    #linux #nixos #pointcloud #lidar

  11. New open‑source breakthrough: SAM3D can isolate individual objects—like a tall lamp—in a point‑cloud scene, far beyond coarse class segmentation. See how promptable segmentation lets you pick out a human, a chair, or any item in a Meta‑style 3D environment. Dive into the details! #SAM3D #PromptableSegmentation #PointCloud #TallLamp

    🔗 aidailypost.com/news/sam3d-iso

  12. What a year it's been!🎉 Feeling immense gratitude and pride as I look back at some of my latest architectural elevation drawings. Each line, every detail, crafted with passion and precision.

    Ready to jump into new challenges and projects! #Architecture #Elevations #Portfolio #2Ddrawing #CADdrafting #geodesy #pointcloud #laserscanning #survey

  13. I released an update to my libE57Format library—version 3.3—to fix some build warnings & to update the build system (cmake)🏗️.

    It is a C++ library which provides read & write support for the ASTM-standard E57 file format on Linux, macOS, and Windows. E57 files store 3D point cloud data, attributes associated with 3D point data (color & intensity), and 2D images (photos).

    github.com/asmaloney/libE57For

  14. More [Canadian] High-Resolution Lidar [HRDEM] And Elevation Data Now Available
    --
    natural-resources.canada.ca/sc <-- shared technical press release
    --
    “... In this first article, highlights include:
    • HRDEM & HRDEM Mosaic - over 709,000 km² of new LiDAR-derived elevation data added since May 2024, increasing coverage by 54%. This product now covers 244 of Canada’s 250 largest cities, and over 95% of the population.
    • Northern HRDEM data - fully updated using ArcticDEM v4.1, improving quality for the entire Canadian Arctic.
    • Automatically Extracted Buildings - Added 61 new projects and over 2.58 million building footprints, bringing the total to over 13.6 million.
    • LiDAR Point Clouds - Expanded by over 200,000 km2, now totalling close to 364,000 km²…”
    #GIS #spatial #mapping #Canada #HRDEM #mosaic #LiDAR #elevation #NationalElevationDataStrategy #pointcloud #ArcticDEM #building #footprints #geographic #coverage #progress #opendata #Canadian #arctic #remotesensing #earthobservation #NaturalResourcesCanada

  15. More [Canadian] High-Resolution Lidar [HRDEM] And Elevation Data Now Available
    --
    natural-resources.canada.ca/sc <-- shared technical press release
    --
    “... In this first article, highlights include:
    • HRDEM & HRDEM Mosaic - over 709,000 km² of new LiDAR-derived elevation data added since May 2024, increasing coverage by 54%. This product now covers 244 of Canada’s 250 largest cities, and over 95% of the population.
    • Northern HRDEM data - fully updated using ArcticDEM v4.1, improving quality for the entire Canadian Arctic.
    • Automatically Extracted Buildings - Added 61 new projects and over 2.58 million building footprints, bringing the total to over 13.6 million.
    • LiDAR Point Clouds - Expanded by over 200,000 km2, now totalling close to 364,000 km²…”

  16. Deep-Learning-Based Object Detection And Tracking Of Debris Flows In 3D Through Lidar-Camera Fusion
    --
    doi.org/10.1109/TGRS.2025.3609 <-- shared paper
    --
    [As an engineering geologist, I have been fortunate enough (sic) to see debris flows in action (indeed, I was rerouted on the motorcycle because of them) on the Poudre Canyon Road in Colorado, after the 2020 Cameron Peak Fire]
    #GIS #spatial #mapping #debrisflow #geology #engineeringgeology #geology #massmovement #postfire #wildfire #water #sediment #hydrology #extremeweather #model #modeling #AI #deepleaning #algorithms #objectdection #monitoring #remotesensing #LiDAR #pointcloud #landform #geomorphometry #video #massmovement #risk #hazard #assessment #damage #infrastructure #rock #boulder #cost #economics #realtime #3D #wave #flow #dynamics

  17. Deep-Learning-Based Object Detection And Tracking Of Debris Flows In 3D Through Lidar-Camera Fusion
    --
    doi.org/10.1109/TGRS.2025.3609 <-- shared paper
    --
    [As an engineering geologist, I have been fortunate enough (sic) to see debris flows in action (indeed, I was rerouted on the motorcycle because of them) on the Poudre Canyon Road in Colorado, after the 2020 Cameron Peak Fire]

  18. OpenGeoAI - Artificial Intelligence for Geospatial Data
    --
    opengeoai.org/ <-- OpenGeoAI home page
    --
    github.com/opengeos/geoai <-- shared #GitHub #repository
    --
    “[A] powerful new capability is coming to the GeoAI Python package: finding similar features in remote sensing imagery with DINOv3.
    This feature leverages the DINOv3 pre-trained model to find similar features in remote sensing imagery with a single click…”
    #GIS #spatial #mapping #dinov3 #GeoAI #AI #python #library #github #remotesensing #processing #imagery #comparasion #machinelearning #LiDAR #pointcloud #vector #model #modeling #earthobservation #software #opensource #geoai

  19. OpenGeoAI - Artificial Intelligence for Geospatial Data
    --
    opengeoai.org/ <-- OpenGeoAI home page
    --
    github.com/opengeos/geoai <-- shared
    --
    “[A] powerful new capability is coming to the GeoAI Python package: finding similar features in remote sensing imagery with DINOv3.
    This feature leverages the DINOv3 pre-trained model to find similar features in remote sensing imagery with a single click…”

  20. 🚀 Today, I'm running a workshop on #PointCloud Processing in #QGIS at #FOSS4GEurope 2025 in #Mostar! We'll dive into the newest features in QGIS. 💻 Can’t join in person? Free access at @gisopencourseware.bsky.social: courses.gisopencourseware.org

  21. 🚀 Today, I'm running a workshop on #PointCloud Processing in #QGIS at #FOSS4GEurope 2025 in #Mostar! We'll dive into:
    🔹️ Downloading & preprocessing data
    🔹️ Creating DSMs with interpolation
    🔹️ 3D visualization & styling
    🔹️ Elevation profiles & filtering
    🔹️ Automation with #PDAL Wrench
    🔹️ Editing point clouds

    💻 Can’t join in person? Free access at @gisocw
    🔗courses.gisopencourseware.org/

  22. Every once in a while I get to play with #Blender #b3d on a weekend. These are my favorite days! Here is the result of messing around with a #pointcloud #scan of a historic saw mill in my home town, then messing with things in #GeometryNodes

  23. Sie möchten eine PointCloud mit CloudCompare bearbeiten, wissen aber nicht, wie Sie starten?☁️

    Unser Azubi Jannik hat genau dafür ein leicht verständliches Tutorial geschrieben!
    Von der Installation unter Linux bis zum Zuschneiden, Einfärben und Speichern Ihrer Punktwolke – alles Schritt für Schritt und mit vielen Screenshots. Perfekt für alle, die mit 3D-Daten arbeiten wollen. 🗺️

    Jetzt im Blog lesen: 👇
    wheregroup.com/blog/details/ve

    #PointCloud #CloudCompare #Tutorial #OpenSource #MapComponents

  24. If You’re 3D Scanning, You’ll Want a Way To Work with Point Clouds - 3D scanning is becoming much more accessible, which means it’s more likely that th... - hackaday.com/2025/04/05/if-you #softwarehacks #3dscanning #pointcloud #software #mesh

  25. #PointCloud on the world map – now live with #MapComponents!

    Our new demo shows how a point cloud can be placed on a map and dynamically adjusted. With the GPU-powered deck.gl PointCloudLayer and seamless MapComponents integration, analyzing large data sets is effortless.

    🔹 Dynamic PointCloud customization
    🔹 Connecting #deckgl with MapLibre
    🔹 Full compatibility with MapComponents

    👉 Try it now: catalogue.mapcomponents.org/de
    📚 Learn more: github.com/mapcomponents/react