2022
DOI: 10.48550/arxiv.2207.09084
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Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation

Abstract: Weakly supervised point cloud segmentation, i.e. semantically segmenting a point cloud with only a few labeled points in the whole 3D scene, is highly desirable due to the heavy burden of collecting abundant dense annotations for the model training. However, existing methods remain challenging to accurately segment 3D point clouds since limited annotated data may lead to insufficient guidance for label propagation to unlabeled data. Considering the smoothness-based methods have achieved promising progress, in … Show more

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