MotorFactory：用于小型电动机的大型数据集生成的Blender插件
MotorFactory: A Blender Add-on for Large Dataset Generation of Small Electric Motors
摘要：
为了使自动拆卸不同的产品类型与不确定的条件和程度的磨损再制造，敏捷生产系统，可以动态适应不断变化的需求是必要的。可以采用机器学习算法，因为它们具有从各种类型和变体的产品中学习的泛化能力。然而，在现实中，具有多样性的样本，可以用来训练模型的数据集是很难获得在初始阶段。当系统试图在将来适应新的看不见的输入数据时，这可能导致不良性能。为了生成用于不同学习目的的大型数据集，在我们的项目中，我们提出了一个名为MotorFactory的Blender插件，用于生成各种电机实例的自定义网格模型。MotorFactory允许创建网格模型，这些模型与其他附加组件一起补充，可进一步用于创建合成RGB图像，深度图像，正常图像，分割地面真实掩模和具有逐点语义标签的3D点云数据集。所创建的合成数据集可用于各种任务，包括电机类型分类、用于分散式材料转移任务的目标检测、用于拆卸和处理任务的零件分割，甚至是基于强化学习的机器人控制或视图规划。
英文摘要：
To enable automatic disassembly of different product types with uncertain conditions and degrees of wear in remanufacturing, agile production systems that can adapt dynamically to changing requirements are needed. Machine learning algorithms can be employed due to their generalization capabilities of learning from various types and variants of products. However, in reality, datasets with a diversity of samples that can be used to train models are difficult to obtain in the initial period. This may cause bad performances when the system tries to adapt to new unseen input data in the future. In order to generate large datasets for different learning purposes, in our project, we present a Blender add-on named MotorFactory to generate customized mesh models of various motor instances. MotorFactory allows to create mesh models which, complemented with additional add-ons, can be further used to create synthetic RGB images, depth images, normal images, segmentation ground truth masks, and 3D point cloud datasets with point-wise semantic labels. The created synthetic datasets may be used for various tasks including motor type classification, object detection for decentralized material transfer tasks, part segmentation for disassembly and handling tasks, or even reinforcement learning-based robotics control or view-planning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
