  Learning robust 3D shape segmentation functions with deep neural networks has
emerged as a powerful paradigm, offering promising performance in producing a
consistent part segmentation of each 3D shape. Generalizing across 3D shape
segmentation functions requires robust learning of priors over the respective
function space and enables consistent part segmentation of shapes in presence
of significant 3D structure variations. Existing generalization methods rely on
extensive training of 3D shape segmentation functions on large-scale labeled
datasets. In this paper, we proposed to formalize the learning of a 3D shape
segmentation function space as a meta-learning problem, aiming to predict a 3D
segmentation model that can be quickly adapted to new shapes with no or limited
training data. More specifically, we define each task as unsupervised learning
of shape-conditioned 3D segmentation function which takes as input points in 3D
space and predicts the part-segment labels. The 3D segmentation function is
trained by a self-supervised 3D shape reconstruction loss without the need for
part labels. Also, we introduce an auxiliary deep neural network as a
meta-learner which takes as input a 3D shape and predicts the prior over the
respective 3D segmentation function space. We show in experiments that our
meta-learning approach, denoted as Meta-3DSeg, leads to improvements on
unsupervised 3D shape segmentation over the conventional designs of deep neural
networks for 3D shape segmentation functions.
