High-fidelity datasets play a pivotal role in imbuing simulators with realism, enabling the benchmarking of various state-of-the-art deep inference models. These models are particularly instrumental in tasks such as semantic segmentation, classification, and localization. This study showcases the efficacy of a customized manufacturing dataset comprising 60 classes in the creation of a high-fidelity digital twin of a robotic manipulation environment. By leveraging the concept of transfer learning, different 6D pose estimation models are trained within the simulated environment using domain randomization and subsequently tested on real-world objects to assess domain adaptation. To ascertain the effectiveness and realism of the created data-set, pose accuracy and mean absolute error (MAE) metrics are reported to quantify the model2real gap.

翻译：摘要：高保真数据集在赋予模拟器真实感方面起着关键作用，为各类先进深度推断模型的基准测试提供支持，尤其适用于语义分割、分类和定位等任务。本研究展示了一个包含60类物体的定制化制造数据集在构建机器人操作环境高保真数字孪生中的有效性。通过利用迁移学习概念，在模拟环境中使用域随机化方法训练多种6D姿态估计模型，并随后在真实物体上进行测试以评估域自适应能力。为验证所创建数据集的真实性与有效性，报告了姿态精度和平均绝对误差指标来量化模型到现实的差距。