  Zero-shot learning on 3D point cloud data is a related underexplored problem
compared to its 2D image counterpart. 3D data brings new challenges for ZSL due
to the unavailability of robust pre-trained feature extraction models. To
address this problem, we propose a prompt-guided 3D scene generation and
supervision method that augments 3D data to learn the network better, exploring
the complex interplay of seen and unseen objects. First, we merge point clouds
of two 3D models in certain ways described by a prompt. The prompt acts like
the annotation describing each 3D scene. Later, we perform contrastive learning
to train our proposed architecture in an end-to-end manner. We argue that 3D
scenes can relate objects more efficiently than single objects because popular
language models (like BERT) can achieve high performance when objects appear in
a context. Our proposed prompt-guided scene generation method encapsulates data
augmentation and prompt-based annotation/captioning to improve 3D ZSL
performance. We have achieved state-of-the-art ZSL and generalized ZSL
performance on synthetic (ModelNet40, ModelNet10) and real-scanned
(ScanOjbectNN) 3D object datasets.
