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Point Cloud Understanding with UniRepLKNet

Created by Xiaohan Ding, Yiyuan Zhang, etc.

This repository is an official implementation of UniRepLKNet.

This repository is built to explore the ability of RepLK-series networks to understand point cloud. We are mainly focused on the shape classification with ModelNet-40 and ScanObjectNN datasets. Besides fully training, we also explore the advantages of pretrained UniRepLKNet on few-shot learning tasks.

Preparation

Installation Prerequisites

  • Python 3.9
  • CUDA 11.3
  • PyTorch 1.11.1
  • timm 0.5.4
  • torch_scatter
  • pointnet2_ops
  • cv2, sklearn, yaml, h5py
conda create -n pt python=3.9
conda activate pt
conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3

mkdir lib
cd lib
git clone https://github.com/erikwijmans/Pointnet2_PyTorch.git
cd Pointnet2_PyTorch
pip install pointnet2_ops_lib/.
cd ../..

pip install torch-scatter -f https://data.pyg.org/whl/torch-1.11.0+cu113.html
pip install timm==0.5.4 opencv-python scikit-learn h5py pyyaml tqdm tensorboardx einops

Data Preparation

  • Download the processed ModelNet40 dataset from [Google Drive][Tsinghua Cloud][BaiDuYun](code:4u1e). Or you can download the offical ModelNet from here, and process it by yourself.

  • Download the official ScanObjectNN dataset from here.

  • The data is expected to be in the following file structure:

    Point/
|-- config/
|-- data/
    |-- ModelNet40/
        |-- modelnet40_shape_names.txt
        |-- modelnet_train.txt
        |-- modelnet_test.txt
        |-- modelnet40_train_8192pts_fps.dat
        |-- modelnet40_test_8192pts_fps.dat
    |-- ScanObjectNN/
        |-- main_split/
            |-- training_objectdataset_augmentedrot_scale75.h5
            |-- test_objectdataset_augmentedrot_scale75.h5
|-- dataset/

(modelnet40_shape_names.txt, modelnet_train.txt, and modelnet_test.txt are provided in PointBERT )

Usage

bash tool/train_unireplknet.sh mv_unireplket-s ModelNet40 config/ModelNet40/multiview_UniRepLKNet-S.yaml

Acknowledgements

Our code is inspired by Meta-Transformer and P2P.