无需语义数据，用几何关系生成组件装配图谱
Geometric 2D Scene Graph Generation
- 基于Faster R-CNN的几何表征+Transformer生成连接矩阵
- 在小样本玩具组件数据集上实现92.3%装配关系准确率
- 适合机器人装配理解与少样本场景建模应用
在消费产品生产流程中，装配说明对规划与执行至关重要。类似地，装配机器人需理解组件间的组合关系。为此，本文提出一种构建场景图的方法，用于表征组件间的装配关系。该方法不依赖语义数据，可处理极小数据集。首先利用Faster R-CNN输出生成几何表示，再通过Transformer架构生成邻接矩阵。该矩阵输入至基于注意力图卷积网络（aGCN）的消息传递型孪生网络，以刻画组件间连接。我们在一个由玩具模型组件构成的实验数据集上验证方法，这些组件可组装成交通工具。结果表明，该方法在小样本条件下仍能有效捕捉装配关系。
原文摘要 · Abstract (English)
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.
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