论文标题
通过多型学习弱监督的3D点云进行分割
Weakly Supervised 3D Point Cloud Segmentation via Multi-Prototype Learning
论文作者
论文摘要
解决3D点云细分中的注释挑战启发了对弱监督学习的研究。现有方法主要集中于利用歧管和伪标记以利用大型未标记的数据点。这里的一个基本挑战在于局部几何结构的较大类内变化，导致语义类别内的子类。在这项工作中，我们利用这种直觉并选择为每个子类维护单个分类器。从技术上讲，我们设计了一个多型分类器，每个原型都用作一个子类的分类器权重。为了实现多型分类器权重的有效更新，我们分别提出了两个约束，以更新原型W.R.T.所有点功能，并鼓励学习各种原型。对弱监督的3D点云分段任务进行的实验验证了所提出的方法的功效，尤其是在低标签状态下。考虑到语义亚类的一致发现，我们的假设也得到了验证。
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. Technically, we design a multi-prototype classifier, each prototype serves as the classifier weights for one subclass. To enable effective updating of multi-prototype classifier weights, we propose two constraints respectively for updating the prototypes w.r.t. all point features and for encouraging the learning of diverse prototypes. Experiments on weakly supervised 3D point cloud segmentation tasks validate the efficacy of proposed method in particular at low-label regime. Our hypothesis is also verified given the consistent discovery of semantic subclasses at no cost of additional annotations.
