https://github.com/townim-faisal/lwf-3D

[IWANN 2021] Reducing catastrophic forgetting in 3D point cloud objects with help of semantic information
https://github.com/townim-faisal/lwf-3D

3d-point-clouds catastrophic-forgetting continual-learning

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[IWANN 2021] Reducing catastrophic forgetting in 3D point cloud objects with help of semantic information

• Host: GitHub
• URL: https://github.com/townim-faisal/lwf-3D
• Owner: townim-faisal
• Created: 2021-06-07T17:10:07.000Z (about 3 years ago)
• Default Branch: master
• Last Pushed: 2021-06-08T04:51:39.000Z (about 3 years ago)
• Last Synced: 2024-01-18T15:51:53.757Z (6 months ago)
• Topics: 3d-point-clouds, catastrophic-forgetting, continual-learning
• Language: Python
• Homepage:
• Size: 19.5 KB
• Stars: 4
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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• awesome-stars - townim-faisal/lwf-3D - [IWANN 2021] Reducing catastrophic forgetting in 3D point cloud objects with help of semantic information (Python)

README

        
# Learning without Forgetting for 3D Point Cloud Objects

## Requirements

Install necessary packages from `requirements.txt` [file](./requirements.txt).

## Data

Semantic embedding for the dataset will be found [here](https://drive.google.com/drive/folders/1rqRyRI_i3MVRd_cLEBlmNNvggGBO-lEl?usp=sharing). You will find the class split in the paper.

## Model

Pretrained model of old task: [here](https://drive.google.com/drive/folders/1rfJQXXCOts024vnBisD5ITCaGRSVeKSt?usp=sharing)

Pretrained model of new task: [here](https://drive.google.com/drive/folders/1mYmwUTHscR3pdP8MJGHGM7bwO1VWlZqy?usp=sharing)

## Training and Evaluation

For each dataset, there is a corresponding configuration files located in `config` folder. Below is the description of configuration file.

```

seen_class : number of classes for old task

unseen_class : number of classes for new task

total_class : number of total classes

dataset_path : path of the dataset i.e. "content/ModelnetNew"

saved_model : folder to save model for new task

batch_size : batch size

lr : learning rate

wd : weighting decay

T: temperature for KD loss

pointnet_old_model_path_none: model path for old task using pointnet (no semantic information)

pointnet_old_model_path_w2v: model path for old task using pointnet and word2vec

pointnet_old_model_path_glove: model path for old task using pointnet and glove

pointconv_old_model_path_none: model path for old task using pointconv (no semantic information)

pointconv_old_model_path_w2v: model path for old task using pointconv and word2vec

pointconv_old_model_path_glove: model path for old task using pointconv and glove

dgcnn_old_model_path_none: model path for old task using dgcnn (no semantic information)

dgcnn_old_model_path_w2v:  model path for old task using dgcnn and word2vec

dgcnn_old_model_path_glove: model path for old task using dgcnn and glove

```

For training and evaluating, arguments for each python script are:

```

--dataset: ModelNet, ScanObjectNN

--epoch: number of epochs 

--sem: using semantic representation i.e. w2v, glove, none

```

## Acknowledgements

This implementation has been based on these repositories: [PointNet](https://github.com/yanx27/Pointnet_Pointnet2_pytorch), [PointConv](https://github.com/DylanWusee/pointconv_pytorch) and [DGCNN](https://github.com/WangYueFt/dgcnn/tree/master/pytorch).

## Citation

```

@inproceedings{lwf3D2021,

  title={Learning without Forgetting for 3D Point Cloud Objects},

  author={Townim Chowdhury, Mahira Jalisha, Ali Cheraghian, and Shafin Rahman},

  booktitle = {International Work-Conference on Artificial Neural Networks (IWANN)},

  year={2021}

}

```