  Though deep learning methods have shown great success in 3D point cloud part
segmentation, they generally rely on a large volume of labeled training data,
which makes the model suffer from unsatisfied generalization abilities to
unseen classes with limited data. To address this problem, we present a novel
meta-learning strategy that regards the 3D shape segmentation function as a
task. By training over a number of 3D part segmentation tasks, our method is
capable to learn the prior over the respective 3D segmentation function space
which leads to an optimal model that is rapidly adapting to new part
segmentation tasks. To implement our meta-learning strategy, we propose two
novel modules: meta part segmentation learner and part segmentation learner.
During the training process, the part segmentation learner is trained to
complete a specific part segmentation task in the few-shot scenario. In the
meantime, the meta part segmentation learner is trained to capture the prior
from multiple similar part segmentation tasks. Based on the learned information
of task distribution, our meta part segmentation learner is able to dynamically
update the part segmentation learner with optimal parameters which enable our
part segmentation learner to rapidly adapt and have great generalization
ability on new part segmentation tasks. We demonstrate that our model achieves
superior part segmentation performance with the few-shot setting on the widely
used dataset: ShapeNet.
