  3D point cloud semantic segmentation aims to group all points into different
semantic categories, which benefits important applications such as point cloud
scene reconstruction and understanding. Existing supervised point cloud
semantic segmentation methods usually require large-scale annotated point
clouds for training and cannot handle new categories. While a few-shot learning
method was proposed recently to address these two problems, it suffers from
high computational complexity caused by graph construction and inability to
learn fine-grained relationships among points due to the use of pooling
operations. In this paper, we further address these problems by developing a
new multi-layer transformer network for few-shot point cloud semantic
segmentation. In the proposed network, the query point cloud features are
aggregated based on the class-specific support features in different scales.
Without using pooling operations, our method makes full use of all pixel-level
features from the support samples. By better leveraging the support features
for few-shot learning, the proposed method achieves the new state-of-the-art
performance, with 15\% less inference time, over existing few-shot 3D point
cloud segmentation models on the S3DIS dataset and the ScanNet dataset.
