Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Clouds

Bjoern Michele   Alexandre Boulch   Gilles Puy    Maxime Bucher   Renaud Marlet  

3DV 2021

Abstract

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.

BibTeX

@inproceedings{michele2021generative,
  title={Generative Zero-Shot Learning for Semantic Segmentation of {3D} Point Cloud},
  author={Michele, Bj{\"o}rn and Boulch, Alexandre and Puy, Gilles and Bucher, Maxime and Marlet, Renaud},
  booktitle={International Conference on 3D Vision (3DV)},
  year={2021}
}