几何二维场景图生成
Geometric 2D Scene Graph Generation
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中文总结 AI 辅助
针对消费品生产及机器人装配中部件装配关系表示问题，提出不依赖语义数据、可处理小数据集的方法。利用Faster R-CNN输出经处理生成邻接矩阵，输入基于aGCN架构的暹罗网络表征连接，在玩具模型部件数据集上验证了该方法。
中文摘要 AI 辅助
在消费品生产过程中，装配说明对规划和执行生产过程至关重要。在机器人领域，装配机器人理解部件如何装配同样关键。为此，我们贡献了一种构建场景图的方法来表示和描述部件间的装配关系。该方法不依赖语义数据且能处理小数据集。利用Faster R-CNN模型输出创建几何表示，经变压器架构处理生成邻接矩阵，作为基于注意力图卷积网络（aGCN）架构的暹罗网络输入来表征部件连接。我们在可组装成运输车辆的玩具模型部件研究数据集上验证了该方法。
英文摘要
In production processes for consumer products, assembly instructions are essential not only for planning but also for executing the production process. Likewise in robotics, it is crucial for an assembly robot to understand how components fit together and can be assembled. To facilitate these tasks, we contribute a method for constructing scene graphs to represent and characterize assembly relationships between components. Our approach does not rely on semantic data and is capable of handling a very small dataset. To realize this, the output of a Faster R-CNN model is used to create geometric representations, which are then processed by a transformer architecture to generate an adjacency matrix. This matrix serves as input to a Siamese network that uses message passing based on an attentional graph convolutional network (aGCN) architecture to characterize the connections between the components. We validate our method on a study dataset of toy model components which can be assembled into transportation vehicles.
发表机构
- Mercedes-Benz AG（梅赛德斯-奔驰股份公司）
- Department of Computer and Information Science, University of Konstanz（康斯坦茨大学计算机与信息科学系）
机构由 AI 辅助整理，请以论文原文为准。
