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PointGPT

PointGPT: Auto-regressively Generative Pre-training from Point Clouds ArXiv

In this work, we present PointGPT, a novel approach that extends the concept of GPT to point clouds, utilizing a point cloud auto-regressive generation task for pre-training transformer models. In object classification tasks, our PointGPT achieves 94.9% accuracy on the ModelNet40 dataset and 93.4% accuracy on the ScanObjectNN dataset, outperforming all other transformer models. In few-shot learning tasks, our method also attains new SOTA performance on all four benchmarks.

News

[2023.06.20] Code and the PointGPT-S models have been released!

1. Requirements

PyTorch >= 1.7.0; python >= 3.7; CUDA >= 9.0; GCC >= 4.9; torchvision;

pip install -r requirements.txt

# Chamfer Distance & emd
cd ./extensions/chamfer_dist
python setup.py install --user
cd ./extensions/emd
python setup.py install --user
# PointNet++
pip install "git+https://github.com/erikwijmans/Pointnet2_PyTorch.git#egg=pointnet2_ops&subdirectory=pointnet2_ops_lib"
# GPU kNN
pip install --upgrade https://github.com/unlimblue/KNN_CUDA/releases/download/0.2/KNN_CUDA-0.2-py3-none-any.whl

2. Datasets

We use ShapeNet, ScanObjectNN, ModelNet40 and ShapeNetPart for pre-training the PointGPT-S model. See DATASET.md for details.

3. PointGPT Models

PointGPT-S Models

Task	Dataset	Config	Acc.	Download
Pre-training	ShapeNet	pretrain.yaml	N.A.	here
Classification	ScanObjectNN	finetune_scan_hardest.yaml	86.9%	here
Classification	ScanObjectNN	finetune_scan_objbg.yaml	91.6%	here
Classification	ScanObjectNN	finetune_scan_objonly.yaml	90.0%	here
Classification	ModelNet40(1k)	finetune_modelnet.yaml	94.0%	here
Classification	ModelNet40(8k)	finetune_modelnet_8k.yaml	94.2%	here
Part segmentation	ShapeNetPart	segmentation	86.2% mIoU	here

Task	Dataset	Config	5w10s Acc. (%)	5w20s Acc. (%)	10w10s Acc. (%)	10w20s Acc. (%)
Few-shot learning	ModelNet40	fewshot.yaml	96.8 ± 2.0	98.6 ± 1.1	92.6 ± 4.6	95.2 ± 3.4

PointGPT-B and PointGPT-L will be released soon!

4. PointGPT Pre-training

To pretrain PointGPT on ShapeNet training set, run the following command.

CUDA_VISIBLE_DEVICES=<GPU> python main.py --config cfgs/pretrain.yaml --exp_name <output_file_name>

5. PointGPT Fine-tuning

Fine-tuning on ScanObjectNN, run the following command:

CUDA_VISIBLE_DEVICES=<GPUs> python main.py --config cfgs/finetune_scan_hardest.yaml \
--finetune_model --exp_name <output_file_name> --ckpts <path/to/pre-trained/model>

Fine-tuning on ModelNet40, run the following command:

CUDA_VISIBLE_DEVICES=<GPUs> python main.py --config cfgs/finetune_modelnet.yaml \
--finetune_model --exp_name <output_file_name> --ckpts <path/to/pre-trained/model>

Voting on ModelNet40, run the following command:

CUDA_VISIBLE_DEVICES=<GPUs> python main.py --test --config cfgs/finetune_modelnet.yaml \
--exp_name <output_file_name> --ckpts <path/to/best/fine-tuned/model>

Few-shot learning, run the following command:

CUDA_VISIBLE_DEVICES=<GPUs> python main.py --config cfgs/fewshot.yaml --finetune_model \
--ckpts <path/to/pre-trained/model> --exp_name <output_file_name> --way <5 or 10> --shot <10 or 20> --fold <0-9>

Part segmentation on ShapeNetPart, run the following command:

cd segmentation
python main.py --ckpts <path/to/pre-trained/model> --root path/to/data --learning_rate 0.0002 --epoch 300

6. Visualization

Visulization of pre-trained model on ShapeNet validation set, run:

python main_vis.py --test --ckpts <path/to/pre-trained/model> --config cfgs/pretrain.yaml --exp_name <name>

Acknowledgements

Our codes are built upon Point-MAE, Point-BERT, Pointnet2_PyTorch and Pointnet_Pointnet2_pytorch

Reference

@article{chen2023pointgpt,
  title={PointGPT: Auto-regressively Generative Pre-training from Point Clouds},
  author={Chen, Guangyan and Wang, Meiling and Yang, Yi and Yu, Kai and Yuan, Li and Yue, Yufeng},
  journal={arXiv preprint arXiv:2305.11487},
  year={2023}
}