Transductive Zero-Shot Learning for 3D Point Cloud Classification

Created by Ali Cheraghian from Australian National University.

Introduction

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 not been 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.

Train & Test codes

Coming soon ...

Evaluation protocols

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

Feature vector

You can download the feature vectors, which are extracted from PointNet, PointAugment, DGCNN, and PointConv architecures, of ModelNet, McGill, and ScanObjectNN datasets from the following link,

feature vectors of ModelNet, McGill, and ScanObjectNN datasets

Citation

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}
}

Reference

[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.

[3] A. Cheraghian, S. Rahman, D. Campbell, and L. Petersson. Transductive Zero-Shot Learning for 3D Point Cloud Classification. In Winter Conference on Applications of Computer Vision (WACV), 2020.