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High-fidelity datasets play a pivotal role in imbuing simulators withrealism, enabling the benchmarking of various state-of-the-art deep inferencemodels. These models are particularly instrumental in tasks such as semanticsegmentation, classification, and localization. This study showcases theefficacy of a customized manufacturing dataset comprising 60 classes in thecreation of a high-fidelity digital twin of a robotic manipulation environment.By leveraging the concept of transfer learning, different 6D pose estimationmodels are trained within the simulated environment using domain randomizationand subsequently tested on real-world objects to assess domain adaptation. Toascertain the effectiveness and realism of the created data-set, pose accuracyand mean absolute error (MAE) metrics are reported to quantify the model2realgap.

Data-Link: High Fidelity Manufacturing Datasets for Model2Real Transfer under Industrial Settings