论文标题
与实例编码变压器的3D部分组装生成
3D Part Assembly Generation with Instance Encoded Transformer
论文作者
论文摘要
希望启用能够自动组装的机器人。对物体部件的结构理解在这项任务中起着至关重要的作用，但仍未探索。在本文中，我们专注于从一组零件几何形状组中设置家具组件的设置，这实质上是一个六高的零件姿势估计问题。我们提出了一个基于多层变压器的框架，该框架涉及零件之间的几何和关系推理，以迭代更新零件。我们仔细设计了一个独特的实例编码，以解决几何相似零件之间的歧义，以便可以区分所有零件。除了从头开始组装外，我们还将我们的框架扩展到一个新任务，称为进程部分组件。类似于家具维护，它要求机器人继续使用未完成的产品，并将其余部分组装成适当的位置。我们的方法在公共部门数据集上的多个指标中的最新指标比当前的最新指标取得了10％以上。广泛的实验和定量比较证明了所提出的框架的有效性。
It is desirable to enable robots capable of automatic assembly. Structural understanding of object parts plays a crucial role in this task yet remains relatively unexplored. In this paper, we focus on the setting of furniture assembly from a complete set of part geometries, which is essentially a 6-DoF part pose estimation problem. We propose a multi-layer transformer-based framework that involves geometric and relational reasoning between parts to update the part poses iteratively. We carefully design a unique instance encoding to solve the ambiguity between geometrically-similar parts so that all parts can be distinguished. In addition to assembling from scratch, we extend our framework to a new task called in-process part assembly. Analogous to furniture maintenance, it requires robots to continue with unfinished products and assemble the remaining parts into appropriate positions. Our method achieves far more than 10% improvements over the current state-of-the-art in multiple metrics on the public PartNet dataset. Extensive experiments and quantitative comparisons demonstrate the effectiveness of the proposed framework.
