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Zero-shot point cloud segmentation aims to make deep models capable ofrecognizing novel objects in point cloud that are unseen in the training phase.Recent trends favor the pipeline which transfers knowledge from seen classeswith labels to unseen classes without labels. They typically align visualfeatures with semantic features obtained from word embedding by the supervisionof seen classes' annotations. However, point cloud contains limited informationto fully match with semantic features. In fact, the rich appearance informationof images is a natural complement to the textureless point cloud, which is notwell explored in previous literature. Motivated by this, we propose a novelmulti-modal zero-shot learning method to better utilize the complementaryinformation of point clouds and images for more accurate visual-semanticalignment. Extensive experiments are performed in two popular benchmarks, i.e.,SemanticKITTI and nuScenes, and our method outperforms current SOTA methodswith 52% and 49% improvement on average for unseen class mIoU, respectively.

See More and Know More: Zero-shot Point Cloud Segmentation via Multi-modal Visual Data