pointMLP-pytorch

Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework

Install

Please ensure that python3.7+ is installed. We suggest user use conda to create a new environment.

Install dependencies

pip install -r requirement.txt

Install CUDA kernels

pip install pointnet2_ops_lib/.

Classification ModelNet40

The dataset will be automatically downloaded, run following command to train

# train pointMLP
python main.py --model pointMLP
# train pointMLP-elite
python main.py --model pointMLPElite
# please add other paramemters as you wish.

By default, it will create a fold named "checkpoints/{modelName}-{msg}-{randomseed}", which includes args.txt, best_checkpoint.pth, last_checkpoint.pth, log.txt, out.txt.

To conduct voting experiments, run

# please modify the msg accrodingly
python voting.py --model pointMLP --msg demo

Classification ScanObjectNN

• Make data folder and download the dataset

cd pointMLP-pytorch/classification_ScanObjectNN
mkdir data
cd data
wget http://103.24.77.34/scanobjectnn/h5_files.zip
unzip h5_files.zip

• Train pointMLP/pointMLPElite by

# train pointMLP
python main.py --model pointMLP
# train pointMLP-elite
python main.py --model pointMLPElite
# please add other paramemters as you wish.

By default, it will create a fold named "checkpoints/{modelName}-{msg}-{randomseed}", which includes args.txt, best_checkpoint.pth, last_checkpoint.pth, log.txt, out.txt.

• To conduct voting experiments, run

# please modify the msg accrodingly
python voting.py --model pointMLP --msg demo

Part segmentation

• Make data folder and download the dataset

cd pointMLP-pytorch/part_segmentation
mkdir data
cd data
wget https://shapenet.cs.stanford.edu/media/shapenetcore_partanno_segmentation_benchmark_v0_normal.zip --no-check-certificate
unzip shapenetcore_partanno_segmentation_benchmark_v0_normal.zip

• Train pointMLP by

# train pointMLP
python main.py --model pointMLP
# please add other paramemters as you wish.

Pre-trained models

Please download the pre-trained models and log files here: [anonymous google drive]