  Recent deep learning architectures can recognize instances of 3D point cloud
objects of previously seen classes quite well. At the same time, current 3D
depth camera technology allows generating/segmenting a large amount of 3D point
cloud objects from an arbitrary scene, for which there is no previously seen
training data. A challenge for a 3D point cloud recognition system is, then, to
classify objects from new, unseen, classes. This issue can be resolved by
adopting a zero-shot learning (ZSL) approach for 3D data, similar to the 2D
image version of the same problem. ZSL attempts to classify unseen objects by
comparing semantic information (attribute/word vector) of seen and unseen
classes. Here, we adapt several recent 3D point cloud recognition systems to
the ZSL setting with some changes to their architectures. To the best of our
knowledge, this is the first attempt to classify unseen 3D point cloud objects
in the ZSL setting. A standard protocol (which includes the choice of datasets
and the seen/unseen split) to evaluate such systems is also proposed. Baseline
performances are reported using the new protocol on the investigated models.
This investigation throws a new challenge to the 3D point cloud recognition
community that may instigate numerous future works.
