thefartchild b7bd9a70cb 更新 'requirements.txt'		1 year ago
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Point-MAE

Masked Autoencoders for Point Cloud Self-supervised Learning, ECCV 2022, ArXiv

In this work, we present a novel scheme of masked autoencoders for point cloud self-supervised learning, termed as Point-MAE. Our Point-MAE is neat and efficient, with minimal modifications based on the properties of the point cloud. In classification tasks, Point-MAE outperforms all the other self-supervised learning methods on ScanObjectNN and ModelNet40. Point-MAE also advances state-of-the-art accuracies by 1.5%-2.3% in the few-shot learning on ModelNet40.

1. Requirements

PyTorch >= 1.7.0 < 1.11.0;
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 in this work. See DATASET.md for details.

3. Point-MAE Models

Task	Dataset	Config	Acc.	Download
Pre-training	ShapeNet	pretrain.yaml	N.A.	here
Classification	ScanObjectNN	finetune_scan_hardest.yaml	85.18%	here
Classification	ScanObjectNN	finetune_scan_objbg.yaml	90.02%	here
Classification	ScanObjectNN	finetune_scan_objonly.yaml	88.29%	here
Classification	ModelNet40(1k)	finetune_modelnet.yaml	93.80%	here
Classification	ModelNet40(8k)	finetune_modelnet_8k.yaml	94.04%	here
Part segmentation	ShapeNetPart	segmentation	86.1% mIoU	here

Task	Dataset	Config	5w10s Acc. (%)	5w20s Acc. (%)	10w10s Acc. (%)	10w20s Acc. (%)
Few-shot learning	ModelNet40	fewshot.yaml	96.3 ± 2.5	97.8 ± 1.8	92.6 ± 4.1	95.0 ± 3.0

4. Point-MAE Pre-training

To pretrain Point-MAE on ShapeNet training set, run the following command. If you want to try different models or masking ratios etc., first create a new config file, and pass its path to --config.

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

5. Point-MAE Fine-tuning

Fine-tuning on ScanObjectNN, run:

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:

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:

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:

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:

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-BERT, Pointnet2_PyTorch and Pointnet_Pointnet2_pytorch

Reference

@misc{pang2022masked,
    title={Masked Autoencoders for Point Cloud Self-supervised Learning},
    author={Yatian Pang and Wenxiao Wang and Francis E. H. Tay and Wei Liu and Yonghong Tian and Li Yuan},
    year={2022},
    eprint={2203.06604},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

Point-MAE

Masked Autoencoders for Point Cloud Self-supervised Learning, ECCV 2022, ArXiv

In this work, we present a novel scheme of masked autoencoders for point cloud self-supervised learning, termed as Point-MAE. Our Point-MAE is neat and efficient, with minimal modifications based on the properties of the point cloud. In classification tasks, Point-MAE outperforms all the other self-supervised learning methods on ScanObjectNN and ModelNet40. Point-MAE also advances state-of-the-art accuracies by 1.5%-2.3% in the few-shot learning on ModelNet40.

1. Requirements

PyTorch >= 1.7.0 < 1.11.0;
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 in this work. See DATASET.md for details.

3. Point-MAE Models

Task	Dataset	Config	Acc.	Download
Pre-training	ShapeNet	pretrain.yaml	N.A.	here
Classification	ScanObjectNN	finetune_scan_hardest.yaml	85.18%	here
Classification	ScanObjectNN	finetune_scan_objbg.yaml	90.02%	here
Classification	ScanObjectNN	finetune_scan_objonly.yaml	88.29%	here
Classification	ModelNet40(1k)	finetune_modelnet.yaml	93.80%	here
Classification	ModelNet40(8k)	finetune_modelnet_8k.yaml	94.04%	here
Part segmentation	ShapeNetPart	segmentation	86.1% mIoU	here

Task	Dataset	Config	5w10s Acc. (%)	5w20s Acc. (%)	10w10s Acc. (%)	10w20s Acc. (%)
Few-shot learning	ModelNet40	fewshot.yaml	96.3 ± 2.5	97.8 ± 1.8	92.6 ± 4.1	95.0 ± 3.0

4. Point-MAE Pre-training

To pretrain Point-MAE on ShapeNet training set, run the following command. If you want to try different models or masking ratios etc., first create a new config file, and pass its path to --config.

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

5. Point-MAE Fine-tuning

Fine-tuning on ScanObjectNN, run:

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:

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:

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:

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:

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-BERT, Pointnet2_PyTorch and Pointnet_Pointnet2_pytorch

Reference

@misc{pang2022masked,
    title={Masked Autoencoders for Point Cloud Self-supervised Learning},
    author={Yatian Pang and Wenxiao Wang and Francis E. H. Tay and Wei Liu and Yonghong Tian and Li Yuan},
    year={2022},
    eprint={2203.06604},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

Point-MAE

Masked Autoencoders for Point Cloud Self-supervised Learning, ECCV 2022, ArXiv

In this work, we present a novel scheme of masked autoencoders for point cloud self-supervised learning, termed as Point-MAE. Our Point-MAE is neat and efficient, with minimal modifications based on the properties of the point cloud. In classification tasks, Point-MAE outperforms all the other self-supervised learning methods on ScanObjectNN and ModelNet40. Point-MAE also advances state-of-the-art accuracies by 1.5%-2.3% in the few-shot learning on ModelNet40.

1. Requirements

PyTorch >= 1.7.0 < 1.11.0;
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 in this work. See DATASET.md for details.

3. Point-MAE Models

Task	Dataset	Config	Acc.	Download
Pre-training	ShapeNet	pretrain.yaml	N.A.	here
Classification	ScanObjectNN	finetune_scan_hardest.yaml	85.18%	here
Classification	ScanObjectNN	finetune_scan_objbg.yaml	90.02%	here
Classification	ScanObjectNN	finetune_scan_objonly.yaml	88.29%	here
Classification	ModelNet40(1k)	finetune_modelnet.yaml	93.80%	here
Classification	ModelNet40(8k)	finetune_modelnet_8k.yaml	94.04%	here
Part segmentation	ShapeNetPart	segmentation	86.1% mIoU	here

Task	Dataset	Config	5w10s Acc. (%)	5w20s Acc. (%)	10w10s Acc. (%)	10w20s Acc. (%)
Few-shot learning	ModelNet40	fewshot.yaml	96.3 ± 2.5	97.8 ± 1.8	92.6 ± 4.1	95.0 ± 3.0

4. Point-MAE Pre-training

To pretrain Point-MAE on ShapeNet training set, run the following command. If you want to try different models or masking ratios etc., first create a new config file, and pass its path to --config.

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

5. Point-MAE Fine-tuning

Fine-tuning on ScanObjectNN, run:

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:

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:

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:

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:

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-BERT, Pointnet2_PyTorch and Pointnet_Pointnet2_pytorch

Reference

@misc{pang2022masked,
    title={Masked Autoencoders for Point Cloud Self-supervised Learning},
    author={Yatian Pang and Wenxiao Wang and Francis E. H. Tay and Wei Liu and Yonghong Tian and Li Yuan},
    year={2022},
    eprint={2203.06604},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

Point-MAE

Masked Autoencoders for Point Cloud Self-supervised Learning, ECCV 2022, ArXiv

In this work, we present a novel scheme of masked autoencoders for point cloud self-supervised learning, termed as Point-MAE. Our Point-MAE is neat and efficient, with minimal modifications based on the properties of the point cloud. In classification tasks, Point-MAE outperforms all the other self-supervised learning methods on ScanObjectNN and ModelNet40. Point-MAE also advances state-of-the-art accuracies by 1.5%-2.3% in the few-shot learning on ModelNet40.

1. Requirements

PyTorch >= 1.7.0 < 1.11.0;
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 in this work. See DATASET.md for details.

3. Point-MAE Models

Task	Dataset	Config	Acc.	Download
Pre-training	ShapeNet	pretrain.yaml	N.A.	here
Classification	ScanObjectNN	finetune_scan_hardest.yaml	85.18%	here
Classification	ScanObjectNN	finetune_scan_objbg.yaml	90.02%	here
Classification	ScanObjectNN	finetune_scan_objonly.yaml	88.29%	here
Classification	ModelNet40(1k)	finetune_modelnet.yaml	93.80%	here
Classification	ModelNet40(8k)	finetune_modelnet_8k.yaml	94.04%	here
Part segmentation	ShapeNetPart	segmentation	86.1% mIoU	here

Task	Dataset	Config	5w10s Acc. (%)	5w20s Acc. (%)	10w10s Acc. (%)	10w20s Acc. (%)
Few-shot learning	ModelNet40	fewshot.yaml	96.3 ± 2.5	97.8 ± 1.8	92.6 ± 4.1	95.0 ± 3.0

4. Point-MAE Pre-training

To pretrain Point-MAE on ShapeNet training set, run the following command. If you want to try different models or masking ratios etc., first create a new config file, and pass its path to --config.

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

5. Point-MAE Fine-tuning

Fine-tuning on ScanObjectNN, run:

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:

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:

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:

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:

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-BERT, Pointnet2_PyTorch and Pointnet_Pointnet2_pytorch

Reference

@misc{pang2022masked,
    title={Masked Autoencoders for Point Cloud Self-supervised Learning},
    author={Yatian Pang and Wenxiao Wang and Francis E. H. Tay and Wei Liu and Yonghong Tian and Li Yuan},
    year={2022},
    eprint={2203.06604},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

Point-MAE

Masked Autoencoders for Point Cloud Self-supervised Learning, ECCV 2022, ArXiv

In this work, we present a novel scheme of masked autoencoders for point cloud self-supervised learning, termed as Point-MAE. Our Point-MAE is neat and efficient, with minimal modifications based on the properties of the point cloud. In classification tasks, Point-MAE outperforms all the other self-supervised learning methods on ScanObjectNN and ModelNet40. Point-MAE also advances state-of-the-art accuracies by 1.5%-2.3% in the few-shot learning on ModelNet40.

1. Requirements

PyTorch >= 1.7.0 < 1.11.0;
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 in this work. See DATASET.md for details.

3. Point-MAE Models

Task	Dataset	Config	Acc.	Download
Pre-training	ShapeNet	pretrain.yaml	N.A.	here
Classification	ScanObjectNN	finetune_scan_hardest.yaml	85.18%	here
Classification	ScanObjectNN	finetune_scan_objbg.yaml	90.02%	here
Classification	ScanObjectNN	finetune_scan_objonly.yaml	88.29%	here
Classification	ModelNet40(1k)	finetune_modelnet.yaml	93.80%	here
Classification	ModelNet40(8k)	finetune_modelnet_8k.yaml	94.04%	here
Part segmentation	ShapeNetPart	segmentation	86.1% mIoU	here

Task	Dataset	Config	5w10s Acc. (%)	5w20s Acc. (%)	10w10s Acc. (%)	10w20s Acc. (%)
Few-shot learning	ModelNet40	fewshot.yaml	96.3 ± 2.5	97.8 ± 1.8	92.6 ± 4.1	95.0 ± 3.0

4. Point-MAE Pre-training

To pretrain Point-MAE on ShapeNet training set, run the following command. If you want to try different models or masking ratios etc., first create a new config file, and pass its path to --config.

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

5. Point-MAE Fine-tuning

Fine-tuning on ScanObjectNN, run:

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:

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:

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:

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:

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-BERT, Pointnet2_PyTorch and Pointnet_Pointnet2_pytorch

Reference

@misc{pang2022masked,
    title={Masked Autoencoders for Point Cloud Self-supervised Learning},
    author={Yatian Pang and Wenxiao Wang and Francis E. H. Tay and Wei Liu and Yonghong Tian and Li Yuan},
    year={2022},
    eprint={2203.06604},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

Point-MAE

Masked Autoencoders for Point Cloud Self-supervised Learning, ECCV 2022, ArXiv

In this work, we present a novel scheme of masked autoencoders for point cloud self-supervised learning, termed as Point-MAE. Our Point-MAE is neat and efficient, with minimal modifications based on the properties of the point cloud. In classification tasks, Point-MAE outperforms all the other self-supervised learning methods on ScanObjectNN and ModelNet40. Point-MAE also advances state-of-the-art accuracies by 1.5%-2.3% in the few-shot learning on ModelNet40.

1. Requirements

PyTorch >= 1.7.0 < 1.11.0;
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 in this work. See DATASET.md for details.

3. Point-MAE Models

Task	Dataset	Config	Acc.	Download
Pre-training	ShapeNet	pretrain.yaml	N.A.	here
Classification	ScanObjectNN	finetune_scan_hardest.yaml	85.18%	here
Classification	ScanObjectNN	finetune_scan_objbg.yaml	90.02%	here
Classification	ScanObjectNN	finetune_scan_objonly.yaml	88.29%	here
Classification	ModelNet40(1k)	finetune_modelnet.yaml	93.80%	here
Classification	ModelNet40(8k)	finetune_modelnet_8k.yaml	94.04%	here
Part segmentation	ShapeNetPart	segmentation	86.1% mIoU	here

Task	Dataset	Config	5w10s Acc. (%)	5w20s Acc. (%)	10w10s Acc. (%)	10w20s Acc. (%)
Few-shot learning	ModelNet40	fewshot.yaml	96.3 ± 2.5	97.8 ± 1.8	92.6 ± 4.1	95.0 ± 3.0

4. Point-MAE Pre-training

To pretrain Point-MAE on ShapeNet training set, run the following command. If you want to try different models or masking ratios etc., first create a new config file, and pass its path to --config.

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

5. Point-MAE Fine-tuning

Fine-tuning on ScanObjectNN, run:

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:

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:

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:

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:

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-BERT, Pointnet2_PyTorch and Pointnet_Pointnet2_pytorch

Reference

@misc{pang2022masked,
    title={Masked Autoencoders for Point Cloud Self-supervised Learning},
    author={Yatian Pang and Wenxiao Wang and Francis E. H. Tay and Wei Liu and Yonghong Tian and Li Yuan},
    year={2022},
    eprint={2203.06604},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

Point-MAE

Masked Autoencoders for Point Cloud Self-supervised Learning, ECCV 2022, ArXiv

In this work, we present a novel scheme of masked autoencoders for point cloud self-supervised learning, termed as Point-MAE. Our Point-MAE is neat and efficient, with minimal modifications based on the properties of the point cloud. In classification tasks, Point-MAE outperforms all the other self-supervised learning methods on ScanObjectNN and ModelNet40. Point-MAE also advances state-of-the-art accuracies by 1.5%-2.3% in the few-shot learning on ModelNet40.

1. Requirements

PyTorch >= 1.7.0 < 1.11.0;
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 in this work. See DATASET.md for details.

3. Point-MAE Models

Task	Dataset	Config	Acc.	Download
Pre-training	ShapeNet	pretrain.yaml	N.A.	here
Classification	ScanObjectNN	finetune_scan_hardest.yaml	85.18%	here
Classification	ScanObjectNN	finetune_scan_objbg.yaml	90.02%	here
Classification	ScanObjectNN	finetune_scan_objonly.yaml	88.29%	here
Classification	ModelNet40(1k)	finetune_modelnet.yaml	93.80%	here
Classification	ModelNet40(8k)	finetune_modelnet_8k.yaml	94.04%	here
Part segmentation	ShapeNetPart	segmentation	86.1% mIoU	here

Task	Dataset	Config	5w10s Acc. (%)	5w20s Acc. (%)	10w10s Acc. (%)	10w20s Acc. (%)
Few-shot learning	ModelNet40	fewshot.yaml	96.3 ± 2.5	97.8 ± 1.8	92.6 ± 4.1	95.0 ± 3.0

4. Point-MAE Pre-training

To pretrain Point-MAE on ShapeNet training set, run the following command. If you want to try different models or masking ratios etc., first create a new config file, and pass its path to --config.

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

5. Point-MAE Fine-tuning

Fine-tuning on ScanObjectNN, run:

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:

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:

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:

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:

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-BERT, Pointnet2_PyTorch and Pointnet_Pointnet2_pytorch

Reference

@misc{pang2022masked,
    title={Masked Autoencoders for Point Cloud Self-supervised Learning},
    author={Yatian Pang and Wenxiao Wang and Francis E. H. Tay and Wei Liu and Yonghong Tian and Li Yuan},
    year={2022},
    eprint={2203.06604},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

Point-MAE

Masked Autoencoders for Point Cloud Self-supervised Learning, ECCV 2022, ArXiv

In this work, we present a novel scheme of masked autoencoders for point cloud self-supervised learning, termed as Point-MAE. Our Point-MAE is neat and efficient, with minimal modifications based on the properties of the point cloud. In classification tasks, Point-MAE outperforms all the other self-supervised learning methods on ScanObjectNN and ModelNet40. Point-MAE also advances state-of-the-art accuracies by 1.5%-2.3% in the few-shot learning on ModelNet40.

1. Requirements

PyTorch >= 1.7.0 < 1.11.0;
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 in this work. See DATASET.md for details.

3. Point-MAE Models

Task	Dataset	Config	Acc.	Download
Pre-training	ShapeNet	pretrain.yaml	N.A.	here
Classification	ScanObjectNN	finetune_scan_hardest.yaml	85.18%	here
Classification	ScanObjectNN	finetune_scan_objbg.yaml	90.02%	here
Classification	ScanObjectNN	finetune_scan_objonly.yaml	88.29%	here
Classification	ModelNet40(1k)	finetune_modelnet.yaml	93.80%	here
Classification	ModelNet40(8k)	finetune_modelnet_8k.yaml	94.04%	here
Part segmentation	ShapeNetPart	segmentation	86.1% mIoU	here

Task	Dataset	Config	5w10s Acc. (%)	5w20s Acc. (%)	10w10s Acc. (%)	10w20s Acc. (%)
Few-shot learning	ModelNet40	fewshot.yaml	96.3 ± 2.5	97.8 ± 1.8	92.6 ± 4.1	95.0 ± 3.0

4. Point-MAE Pre-training

To pretrain Point-MAE on ShapeNet training set, run the following command. If you want to try different models or masking ratios etc., first create a new config file, and pass its path to --config.

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

5. Point-MAE Fine-tuning

Fine-tuning on ScanObjectNN, run:

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:

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:

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:

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:

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-BERT, Pointnet2_PyTorch and Pointnet_Pointnet2_pytorch

Reference

@misc{pang2022masked,
    title={Masked Autoencoders for Point Cloud Self-supervised Learning},
    author={Yatian Pang and Wenxiao Wang and Francis E. H. Tay and Wei Liu and Yonghong Tian and Li Yuan},
    year={2022},
    eprint={2203.06604},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

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