Towards Robust Part-aware Instance Segmentation for Industrial Bin Picking

• URL: http://arxiv.org/abs/2203.02767v1
• Date: Sat, 5 Mar 2022 14:58:05 GMT
• Title: Towards Robust Part-aware Instance Segmentation for Industrial Bin Picking
• Authors: Yidan Feng, Biqi Yang, Xianzhi Li, Chi-Wing Fu, Rui Cao, Kai Chen, Qi Dou, Mingqiang Wei, Yun-Hui Liu, and Pheng-Ann Heng
• Abstract summary: We formulate a novel part-aware instance segmentation pipeline for industrial bin picking. 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. We contribute the first instance segmentation dataset, which contains a variety of industrial objects that are thin and have non-trivial shapes.
• Score: 113.79582950811348
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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