Pointnet.pytorch
pytorch implementation for "PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation" https://arxiv.org/abs/1612.00593
Install / Use
/learn @fxia22/Pointnet.pytorchREADME
PointNet.pytorch
This repo is implementation for PointNet(https://arxiv.org/abs/1612.00593) in pytorch. The model is in pointnet/model.py.
It is tested with pytorch-1.0.
Download data and running
git clone https://github.com/fxia22/pointnet.pytorch
cd pointnet.pytorch
pip install -e .
Download and build visualization tool
cd scripts
bash build.sh #build C++ code for visualization
bash download.sh #download dataset
Training
cd utils
python train_classification.py --dataset <dataset path> --nepoch=<number epochs> --dataset_type <modelnet40 | shapenet>
python train_segmentation.py --dataset <dataset path> --nepoch=<number epochs>
Use --feature_transform to use feature transform.
Performance
Classification performance
On ModelNet40:
| | Overall Acc | | :---: | :---: | | Original implementation | 89.2 | | this implementation(w/o feature transform) | 86.4 | | this implementation(w/ feature transform) | 87.0 |
| | Overall Acc | | :---: | :---: | | Original implementation | N/A | | this implementation(w/o feature transform) | 98.1 | | this implementation(w/ feature transform) | 97.7 |
Segmentation performance
Segmentation on A subset of shapenet.
| Class(mIOU) | Airplane | Bag| Cap|Car|Chair|Earphone|Guitar|Knife|Lamp|Laptop|Motorbike|Mug|Pistol|Rocket|Skateboard|Table | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | Original implementation | 83.4 | 78.7 | 82.5| 74.9 |89.6| 73.0| 91.5| 85.9| 80.8| 95.3| 65.2| 93.0| 81.2| 57.9| 72.8| 80.6| | this implementation(w/o feature transform) | 73.5 | 71.3 | 64.3 | 61.1 | 87.2 | 69.5 | 86.1|81.6| 77.4|92.7|41.3|86.5|78.2|41.2|61.0|81.1| | this implementation(w/ feature transform) | | | | | 87.6 | | | | | | | | | | |81.0|
Note that this implementation trains each class separately, so classes with fewer data will have slightly lower performance than reference implementation.
Sample segmentation result:
Links
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