While there has been a number of studies on Zero-Shot Learning (ZSL) for 2D images, its application to 3D data is still recent and scarce, with just a few methods limited to classification. We present the first generative approach for both 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.

翻译：虽然已经对2D图像的零热学习(ZSL)进行了一些研究,但对3D数据的应用仍然是最近和很少的,只有几种方法限于分类。我们提出了ZSL和通用ZSL(GZSL)在3D数据方面的第一种基因化方法,既可以处理分类,也可以第一次处理语义分解。我们表明,它达到或超过关于诱导ZSL和诱导GZSL的模型Net40分类的先进水平。关于语义分解,我们用S3DIS、扫描网和SmanticKTI为评估这一新任务制定了三个基准。我们的实验表明,我们的方法超过了我们为这项任务提出的强有力的基准。