Few Shot Point Cloud Classification

Few-Shot Point Cloud Classification is a task in the field of computer vision that aims to efficiently classify point cloud data using a small number of labeled samples. This task optimizes learning algorithms to enable models to quickly adapt to new categories with limited training data, thereby improving classification accuracy and generalization capabilities. It is widely applied in scenarios such as autonomous driving, robotic navigation, and 3D scene understanding.