3D point cloud model from the DRZ trainings site
- From the 360° images of the former video ( • German rescue robotic center captured... ) we now generate the 3D point cloud. The UAV needs 3 minutes to capture the outdoor scenario and the hall from inside and outside. The 3D point cloud generation is 5x slower than the video. It uses a VSLAM algorithm to localize the k-frames (green) and with 3 k-frames it use a 360° PatchMatch algorithm implemented at a NVIDIA graphic card (CUDA) to calculated the dense point clouds.The hall ist about 70 x 20 meters.
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From the 360° images of the former video ( • German rescue robotic center captured... ) we now generate the 3D point cloud. The UAV needs 3 minutes to capture the outdoor scenario and the hall from inside and outside. The 3D point cloud generation is 5x slower than the video. It uses a VSLAM algorithm to localize the k-frames (green) and with 3 k-frames it use a 360° PatchMatch algorithm implemented at a NVIDIA graphic card (CUDA) to calculated the dense point clouds.The hall ist about 70 x 20 meters.
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| Verfasserangaben: | Julien Meine, Niklas Voigt, Jan-Niklas Kremer, Niklas Digakis, Max Schulte, Hartmut Surmann |
|---|---|
| URN: | urn:nbn:de:hbz:1010-opus4-43134 |
| Dokumentart: | Video |
| Sprache: | Deutsch |
| Datum der Veröffentlichung (online): | 31.03.2023 |
| Jahr der Erstveröffentlichung: | 2023 |
| Veröffentlichende Institution: | Westfälische Hochschule Gelsenkirchen Bocholt Recklinghausen |
| Datum der Freischaltung: | 26.01.2024 |
| Fachbereiche / Institute: | Fachbereiche / Informatik und Kommunikation |
| DDC-Klassifikation: | Informatik, Informationswissenschaft, allgemeine Werke / Informatik, Wissen, Systeme / Datenverarbeitung; Informatik |
| Lizenz (Deutsch): |  Creative Commons - Namensnennung |
