Created by Ali Cheraghian from Australian National University.

This work is based on our arXiv tech report, which is going to appear in WACV 2020. We proposed a novel transductive zero-shot learning approach for 3D point clouds.

Zero-shot learning, the task of learning to recognize new classes not seen during training, has received considerable attention in the case of 2D image classification. However despite the increasing ubiquity of 3D sensors, the corresponding 3D point cloud classification problem has notbeen meaningfully explored and introduces new challenges. This project extends, for the first time, transductive Zero-Shot Learning (ZSL) and Generalized Zero-Shot Learning (GZSL) approaches to the domain of 3D point cloud classification.

In the "class_name" folder, the class names of seen and unseen sets from all 3D datasets are shown. Also, the "word_vector" folder contains the semantic word vectors of 3D datasets.

Coming soon ...

The evaluation protocols for ZSL and GZSL in this project were introduced by [1] and [2] respectively.

You can download the feature vectors, which are extracted from a pretrained PointNet architecure, of ModelNet, McGill, and SHEREC2015 datasets from the following link,

feature vectors of ModelNet, McGill, and SHEREC2015 datasets using PointNet

If you find our work useful in your research, please consider citing:

@article{cheraghian2019transductive,
  title={Transductive Zero-Shot Learning for 3D Point Cloud Classification},
  author={Ali Cheraghian, Shafin Rahman, Dylan Campbell, and Lars Petersson},
  journal={arXiv preprint arXiv:1912.07161},
  year={2019}
}

[1] A. Cheraghian, S. Rahman, and L. Petersson. Zero-shot learning of 3d point cloud objects. In International Conference on Machine Vision Applications (MVA), 2019.

[2] A. Cheraghian, S. Rahman, D. Campbell, and L. Petersson. Mitigating the hubness problem for zero-shot learning of 3D objects. In British Machine Vision Conference (BMVC), 2019.