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Weakly Supervised 3D Point Cloud Segmentation via Multi-Prototype Learning
[article]
2022
arXiv
pre-print
Addressing the annotation challenge in 3D Point Cloud segmentation has inspired research into weakly supervised learning. Existing approaches mainly focus on exploiting manifold and pseudo-labeling to make use of large unlabeled data points. A fundamental challenge here lies in the large intra-class variations of local geometric structure, resulting in subclasses within a semantic class. In this work, we leverage this intuition and opt for maintaining an individual classifier for each subclass.
arXiv:2205.03137v1
fatcat:bdu7ytk5pjaqnb62dzhtznnlom