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Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation
[article]
2020
Segmenting unseen objects in cluttered scenes is an important skill that robots need to acquire in order to perform tasks in new environments. In this work, we propose a new method for unseen object instance segmentation by learning RGB-D feature embeddings from synthetic data. A metric learning loss function is utilized to learn to produce pixel-wise feature embeddings such that pixels from the same object are close to each other and pixels from different objects are separated in the embedding
doi:10.48550/arxiv.2007.15157
fatcat:l2vc7733arcx3nkxfp6gjfbxga