Parts2Words: Learning Joint Embedding of Point Clouds and Texts by Bidirectional Matching between Parts and Words

Introduction

This is the source code of Parts2Words: Learning Joint Embedding of Point Clouds and Texts by Bidirectional Matching between Parts and Words, an approch for 3D Shape-Text Matching based on local based matching method.

Requirements

pip install -r requirements.txt

Download data

We pack the shapenet data with segmentation annotation in h5 file, you can download the h5 file from here

data
|-- models
|   `-- shapenet
|       
`-- shapenet
    |-- shapenetv2_level_1.pkl
    |-- split_shape_captioner
    |   |-- test_03001627.txt
    |   |-- test_04379243.txt
    |   |-- train_03001627.txt
    |   `-- train_04379243.txt
    `-- vocab
        `-- shapenet.json

Set configure file

set data path in config/parts2words_default.yaml.

Train model

python train.py --config conig/parts2words_default.yaml

evaluate model

python val.py --config conig/parts2words_default.yaml

Reference

If you found this code useful, please cite the following paper:

@article{tang2021parts2words,
  title={parts2words: Learning Joint Embedding of Point Clouds and Text by Matching Parts to Words},
  author={Tang, Chuan and Yang, Xi and Wu, Bojian and Han, Zhizhong and Chang, Yi},
  journal={arXiv preprint arXiv:2107.01872},
  year={2021}
}