  Many existing approaches for 3D point cloud semantic segmentation are fully
supervised. These fully supervised approaches heavily rely on large amounts of
labeled training data that are difficult to obtain and cannot segment new
classes after training. To mitigate these limitations, we propose a novel
attention-aware multi-prototype transductive few-shot point cloud semantic
segmentation method to segment new classes given a few labeled examples.
Specifically, each class is represented by multiple prototypes to model the
complex data distribution of labeled points. Subsequently, we employ a
transductive label propagation method to exploit the affinities between labeled
multi-prototypes and unlabeled points, and among the unlabeled points.
Furthermore, we design an attention-aware multi-level feature learning network
to learn the discriminative features that capture the geometric dependencies
and semantic correlations between points. Our proposed method shows significant
and consistent improvements compared to baselines in different few-shot point
cloud semantic segmentation settings (i.e., 2/3-way 1/5-shot) on two benchmark
datasets. Our code is available at https://github.com/Na-Z/attMPTI.

Computer Vision and Pattern Recognition