Abstract

While there has been a number of studies on Zero-ShotLearning (ZSL) for 2D images, its application to 3D datais still recent and scarce, with just a few methods limited to classification. We present the first generative approach forboth ZSL and Generalized ZSL (GZSL) on 3D data, that can handle both classification and, for the first time, semantic segmentation. We show that it reaches or outperforms the state of the art on ModelNet40 classification for both inductive ZSL and inductive GZSL. For semantic segmentation,we created three benchmarks for evaluating this new ZSL task, using S3DIS, ScanNet and SemanticKITTI. Our experiments show that our method outperforms strong baselines,which we additionally propose for this task.