论文标题
构图原型网络具有多视图比较，用于几个点云语义分段
Compositional Prototype Network with Multi-view Comparision for Few-Shot Point Cloud Semantic Segmentation
论文作者
论文摘要
点云分段是3D视觉中的基本视觉理解任务。完全有监督的点云细分网络通常需要大量的数据，并具有点注释，这很昂贵。在这项工作中，我们介绍了组成原型网络，该网络只能使用少数标记的训练数据进行点云进行分割。受图像中的几种学习文献的启发，我们的网络将标签信息直接从有限的培训数据传输到未标记的测试数据以进行预测。网络将复杂点云数据的表示形式分解为一组局部区域表示，并利用它们来计算视觉概念的组成原型。我们的网络包括一个关键的多视图比较组件，可利用支持集的冗余视图。为了评估所提出的方法，我们创建了一个新的分割基准数据集，扫描仪-6^i $，该数据集于扫描仪数据集上构建。广泛的实验表明，我们的方法具有很大的优势。此外，当我们使用网络在完全监督的点云分割数据集中处理长尾问题时，它也可以有效地提高少数拍摄类的性能。
Point cloud segmentation is a fundamental visual understanding task in 3D vision. A fully supervised point cloud segmentation network often requires a large amount of data with point-wise annotations, which is expensive to obtain. In this work, we present the Compositional Prototype Network that can undertake point cloud segmentation with only a few labeled training data. Inspired by the few-shot learning literature in images, our network directly transfers label information from the limited training data to unlabeled test data for prediction. The network decomposes the representations of complex point cloud data into a set of local regional representations and utilizes them to calculate the compositional prototypes of a visual concept. Our network includes a key Multi-View Comparison Component that exploits the redundant views of the support set. To evaluate the proposed method, we create a new segmentation benchmark dataset, ScanNet-$6^i$, which is built upon ScanNet dataset. Extensive experiments show that our method outperforms baselines with a significant advantage. Moreover, when we use our network to handle the long-tail problem in a fully supervised point cloud segmentation dataset, it can also effectively boost the performance of the few-shot classes.
