Industrial bin picking is a challenging task that requires accurate and
robust segmentation of individual object instances. Particularly, industrial
objects can have irregular shapes, that is, thin and concave, whereas in
bin-picking scenarios, objects are often closely packed with strong occlusion.
To address these challenges, we formulate a novel part-aware instance
segmentation pipeline. The key idea is to decompose industrial objects into
correlated approximate convex parts and enhance the object-level segmentation
with part-level segmentation. We design a part-aware network to predict part
masks and part-to-part offsets, followed by a part aggregation module to
assemble the recognized parts into instances. To guide the network learning, we
also propose an automatic label decoupling scheme to generate ground-truth
part-level labels from instance-level labels. Finally, we contribute the first
instance segmentation dataset, which contains a variety of industrial objects
that are thin and have non-trivial shapes. Extensive experimental results on
various industrial objects demonstrate that our method can achieve the best
segmentation results compared with the state-of-the-art approaches.

针对工业垃圾桶拣选这一具有挑战性的任务，本文提出一种新型的基于部件感知实例分割管道，其中采用部件聚合模块将感知到的部件组装为实例，并且通过产生地面真实部件级标签的自动标签解耦方案来指导网络学习。本文还贡献了第一个实例分割数据集，该数据集包含各种薄且具有非平凡形状的工业物体，并在各种工业物体上进行了广泛的实验，结果表明，与现有的最新方法相比，我们的方法能够实现最佳的分割效果。