ENRICH FEATURES FOR FEW-SHOT POINT CLOUD CLASSIFICATION

Recently, many existing fully supervised methods for pointcloud classification have strongly promoted the developmentof point cloud learning. However, these methods require a lot of labeled data as support, which is challenging to obtain.To alleviate this problem, we propose a novel few-shot point cloud classification method to classify new categories given a few labeled samples. Specifically, we apply the feature supplement module to enrich the geometric information of points and then aggregate multi-scale features through the channel-wise attention module while reducing the computational complexity. Finally, we introduce a classifier to classify the point cloud features under the few-shot learning setup to predict its label. We carry out experimental verification on the benchmark dataset and achieve state-of-the-art performance.