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About this paper
Yidan Feng, Biqi Yang, Xianzhi Li, Chi-Wing Fu, Rui Cao, Kai Chen, Qi Dou, Mingqiang Wei and 2 more
A part-aware instance segmentation framework tackles the challenge of segmenting low-solidity industrial objects in cluttered bin-picking environments by decomposing them into simpler parts. This approach, featuring an automatic label decoupling scheme and a bidirectional part aggregation module, achieves superior segmentation accuracy on the new ILS dataset compared to existing methods.
Key insights
- Decomposing low-solidity, irregularly shaped objects into simpler, more convex parts enables more robust segmentation than treating them as monolithic instances.
- Learning intrinsic geometric relationships between object parts through dedicated offset predictions allows for accurate instance reconstruction even when objects are heavily occluded.
- Automatic generation of part-level training labels using a concavity-based decoupling scheme is effective and addresses the challenge of laborious manual annotation for complex industrial objects.
Summary by alphaXiv.
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