Compositional Prototype Network with Multi-view Comparision for Few-Shot Point Cloud Semantic Segmentation

Understand

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.