Tag: VirtuaLearn3D
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Deep learning with simulated laser scanning data for 3D point cloud classification
Esmorís, A.M., Weiser, H., Winiwarter, L., Cabaleiro, J.C. & Höfle, B. (2024): Deep learning with simulated laser scanning data for 3D point cloud classification. ISPRS Journal of Photogrammetry and Remote Sensing. Vol. 215, pp. 192-213. DOI: 10.1016/j.isprsjprs.2024.06.018 3D point clouds acquired by laser scanning are invaluable for the analysis of geographic phenomena. To extract information…
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VirtuaLearn3D: New Preprint
We have published a preprint of our recent work in the VirtuaLearn3D project! Deep learning with simulated laser scanning data for 3D point cloud classification Esmorís, A.M., Weiser, H., Winiwarter, L., Cabaleiro, J.C. & Höfle, B. (2024) Laser scanning is an active remote sensing technique to acquire state-of-the-art spatial measurements in the form of 3D…
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🦇 Halloween release of HELIOS++, v1.3.0
We proudly present our Halloween release of HELIOS++, Version 1.3.0: https://github.com/3dgeo-heidelberg/helios/releases What’s new in this release? HELIOS++ now supports LiDAR simulation of dynamic scenes. We can now simulate laser scanning of scenes that change during the simulation. This is done by introducing rigid motions, which are defined with XML syntax in the scene XML file.…
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Impressions from Silvilaser 2023
Last week, our PhD student, Hannah Weiser, joined Silvilaser 2023 at University College London (UCL). The conference covers cutting-edge science and technology from the laser scanning and forest communities, which is a perfect match for Hannah’s PhD topic and 3DGeo research in general. The week started off with interesting workshops on Tuesday using some of…