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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Metadaten
Author:Julien Meine, Niklas Voigt, Jan-Niklas Kremer, Niklas Digakis, Max Schulte, Hartmut Surmann
URN:urn:nbn:de:hbz:1010-opus4-43134
Document Type:video
Language:German
Date of Publication (online):2023/03/31
Year of first Publication:2023
Publishing Institution:Westfälische Hochschule Gelsenkirchen Bocholt Recklinghausen
Release Date:2024/01/26
Departments / faculties:Fachbereiche / Informatik und Kommunikation
Dewey Decimal Classification:Informatik, Informationswissenschaft, allgemeine Werke / Informatik, Wissen, Systeme / Datenverarbeitung; Informatik
Licence (German):License LogoCreative Commons - Namensnennung

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