  Zero-shot learning, the task of learning to recognize new classes not seen
during training, has received considerable attention in the case of 2D image
classification. However, despite the increasing ubiquity of 3D sensors, the
corresponding 3D point cloud classification problem has not been meaningfully
explored and introduces new challenges. In this paper, we identify some of the
challenges and apply 2D Zero-Shot Learning (ZSL) methods in the 3D domain to
analyze the performance of existing models. Then, we propose a novel approach
to address the issues specific to 3D ZSL. We first present an inductive ZSL
process and then extend it to the transductive ZSL and Generalized ZSL (GZSL)
settings for 3D point cloud classification. To this end, a novel loss function
is developed that simultaneously aligns seen semantics with point cloud
features and takes advantage of unlabeled test data to address some known
issues (e.g., the problems of domain adaptation, hubness, and data bias). While
designed for the particularities of 3D point cloud classification, the method
is shown to also be applicable to the more common use-case of 2D image
classification. An extensive set of experiments is carried out, establishing
state-of-the-art for ZSL and GZSL on synthetic (ModelNet40, ModelNet10, McGill)
and real (ScanObjectNN) 3D point cloud datasets.
