摘要 : Point-by-point labeling of point clouds is a very costly task. Previous meta-learning-based few-shot methods predict categories by calculating the distance between unlabeled data (query set) and the prototype calculated by a few o... 展开 Point-by-point labeling of point clouds is a very costly task. Previous meta-learning-based few-shot methods predict categories by calculating the distance between unlabeled data (query set) and the prototype calculated by a few of data with the label (support set), which can reduce the dependence of point cloud segmentation algorithms on large amounts of labeled data. But it ignores the category information gap caused by object diversity between the two types of data and forcing information transfer is ineffective. To address this issue, we propose a co-occurrent object mining module for mining co-occurring object information from support and query sets. Specifically, the capture of co-occurrent information is used to activate the feature that co-occurs between the support and query set in the high-dimensional feature space so that the prototype generated by computing the mean of support features is more similar to the query set. By reducing the object diversity within the same category, the information gap problem is gradually improved. In addition, we propose a point-attention module to refine the support set features before mining co-occurrent features. It can be widely embedded in the point cloud backbone network. The experimental results on two semantic segmentation datasets demonstrate that our method obtains an average 19.43% lead over the state-of-the-art methods in 4 different few-shot tasks, while inference is around 45 times faster. 收起