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Generating synthetic LiDAR point cloud data for object detection using the Unreal Game Engine
(2024)
Object detection based on artificial intelligence is ubiquitous in today’s computer vision research and application. The training of the neural networks for object detection requires large and high-quality datasets. Besides datasets based on image data, datasets derived from point clouds offer several advantages. However, training datasets are sparse and their generation requires a lot of effort, especially in industrial domains. A solution to this issue offers the generation of synthetic point cloud data. Based on the design science research method, the work at hand proposes an approach and its instantiation for generating synthetic point cloud data based on the Unreal Engine. The point cloud quality is evaluated by comparing the synthetic cloud to a real-world point cloud. Within a practical example the applicability of the Unreal Game engine for synthetic point cloud generation could be successfully demonstrated.
Veränderte Anforderungen an eine europäische Standardisierungspolitik in der Telekommunikation
(1996)
Veränderte Anforderungen an eine europäische Standardisierungspolitik in der Telekommunikation
(1997)
- Wie kam es zu der globalen Finanzkrise? - Wodurch zeichnet sich die Finanzkrise aus? - Wer hat die Finanzkrise verschuldet? - Welche Rolle spielten Spekulanten? - Inwiefern ist die Finanzkrise selbstverschuldet? - Ist die Globalisierung Schuld an den Finanzkrisen in zahlreichen Regionen dieser Welt? - Wie wirkt sich die Finanzkrise auf die Realwirtschaft aus? - Welche Wege bieten sich, aus der Krise herauszukommen?