  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. This paper extends, for the first time,
transductive Zero-Shot Learning (ZSL) and Generalized Zero-Shot Learning (GZSL)
approaches to the domain of 3D point cloud classification. To this end, a novel
triplet loss is developed that takes advantage of unlabeled test data. While
designed for the task of 3D point cloud classification, the method is also
shown to 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 in the 3D point cloud domain, as well as demonstrating the
applicability of the approach to the image domain.
