Albayrak, Özge BeyzaÖzge BeyzaAlbayrakSchoepflin, DanielDanielSchoepflinHolst, DirkDirkHolstMöller, LarsLarsMöllerSchüppstuhl, ThorstenThorstenSchüppstuhl2023-10-052023-10-05202432nd International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2023978-3-031-38240-6978-3-031-38241-3978-3-031-38242-0https://hdl.handle.net/11420/43558Synthetic data generation to enable industrial visual AI applications is a promising alternative to manual data acquisition. Such methods rely on the availability of 3D models to generate virtual worlds and derive or render 2D image data. Since CAD models can be created on different detail levels but require extensive effort, questions arise about which affect different detail levels of 3D models have on synthetic data generation. The effect of different CAD model details is investigated and compared to different 3D scanning and photogrammetry approaches. Different synthetic datasets are created for each 3D model acquisition type and a test-benchmark set is used to evaluate the performance of each dataset. Based on the results, the suitability of each acquisition method is derived and the effects of bridging the domain gap from the synthetic training domain to the real-world application domain are discussed. The findings indicate that 3D scans are as feasible for synthetic data generation as feature-rich high-level CAD data. Feature poor CAD data as they might originate from manufacturing data performs significantly worse.en3D ScanningData AcquisitionDomain GapPhotogrammetrySynthetic Data GenerationComputer SciencesEngineering and Applied OperationsConference Paper10.1007/978-3-031-38241-3_18Conference Paper