  This paper presents an effective few-shot point cloud semantic segmentation
approach for real-world applications. Existing few-shot segmentation methods on
point cloud heavily rely on the fully-supervised pretrain with large annotated
datasets, which causes the learned feature extraction bias to those pretrained
classes. However, as the purpose of few-shot learning is to handle
unknown/unseen classes, such class-specific feature extraction in pretrain is
not ideal to generalize into new classes for few-shot learning. Moreover, point
cloud datasets hardly have a large number of classes due to the annotation
difficulty. To address these issues, we propose a contrastive self-supervision
framework for few-shot learning pretrain, which aims to eliminate the feature
extraction bias through class-agnostic contrastive supervision. Specifically,
we implement a novel contrastive learning approach with a learnable augmentor
for a 3D point cloud to achieve point-wise differentiation, so that to enhance
the pretrain with managed overfitting through the self-supervision.
Furthermore, we develop a multi-resolution attention module using both the
nearest and farthest points to extract the local and global point information
more effectively, and a center-concentrated multi-prototype is adopted to
mitigate the intra-class sparsity. Comprehensive experiments are conducted to
evaluate the proposed approach, which shows our approach achieves
state-of-the-art performance. Moreover, a case study on practical CAM/CAD
segmentation is presented to demonstrate the effectiveness of our approach for
real-world applications.
