计图点云库
已经实现的模型
| Model |
Classification |
Segmentation |
| PointNet |
√ |
√ |
| PointNet ++ |
√ |
√ |
| PointCNN |
√ |
√ |
| DGCNN |
√ |
√ |
| PointConv |
√ |
√ |
使用方法
安装依赖
sudo apt install python3.7-dev libomp-dev
sudo python3.7 -m pip install git+https://github.com/Jittor/jittor.git
python3.7 -m pip install sklearn lmdb msgpack_numpy
安装点云库
git clone https://github.com/Jittor/PointCloudLib.git # 将库下载的本地
# 您需要将 ModelNet40 和 ShapeNet 数据集下载到 data_util/data/ 里面
ModelNet40 数据集链接 ： https://shapenet.cs.stanford.edu/media/modelnet40_normal_resampled.zip
ShapeNet 数据集链接 ： https://shapenet.cs.stanford.edu/media/shapenet_part_seg_hdf5_data.zip
sh train_cls.sh # 点云分类的训练和测试
sh train_seg.sh # 点云分割的训练和测试
所依赖的库
Python 3.7
Jittor
Numpy
sklearn
lmdb
msgpack_numpy
...
实验结果
分类训练效果测试
| Model |
Input |
overall accuracy |
| PointNet |
1024 xyz |
87.2 |
| PointNet ++ |
4096 xyz + normal |
92.3 |
| PointCNN |
1024 xyz |
92.6 |
| DGCNN |
1024 xyz |
92.9 |
| PointConv |
1024 xyz + normal |
92.4 |
分类训练时间测试
| Model |
Speed up ratio (Compare with Pytorch) |
| PointNet |
1.22 |
| PointNet ++ |
2.72 |
| PointCNN |
2.41 |
| DGCNN |
1.22 |
| PointConv |
|
分割训练效果测试
| Model |
Input |
pIoU |
| PointNet |
2048 xyz + cls label |
83.5 |
| PointNet ++ |
2048 xyz + cls label + normal |
85.0 |
| PointCNN |
2048 xyz + normal |
86.0 |
| DGCNN |
2048 xyz + cls label |
85.1 |
| PointConv |
2048 xyz |
85.4 |
分割训练时间测试
| Model |
Speed up ratio (Compare with Pytorch) |
| PointNet |
1.06 |
| PointNet ++ |
1.85 |
| PointCNN |
None (No pytorch implementation) |
| DGCNN |
1.05 |
| PointConv |
None (No pytorch implementation) |
目录结构
.
├── data_utils # 数据相关工具
│ ├── data # 数据存放路径
│ ├── modelnet40_loader.py
│ └── shapenet_loader.py
├── misc
│ ├── layers.py
│ ├── ops.py
│ ├── pointconv_utils.py
│ └── utils.py
├── networks
│ ├── cls
│ │ ├── dgcnn.py
│ │ ├── pointcnn.py
│ │ ├── pointconv.py
│ │ ├── pointnet2.py
│ │ └── pointnet.py
│ └── seg
│ ├── dgcnn_partseg.py
│ ├── pointcnn_partseg.py
│ ├── pointconv_partseg.py
│ ├── pointnet2_partseg.py
│ └── pointnet_partseg.py
├── README.md
├── run_cls.sh
├── run_partseg.sh
├── train_cls.py
└── train_partseg.py
非常欢迎您使用计图的点云库进行相关的研究，如在使用中有问题，欢迎提交 issus。