Awesome

P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Prompting

Created by Ziyi Wang*, Xumin Yu*, Yongming Rao*, Jie Zhou, Jiwen Lu

This repository is an official implementation of P2P (NeurIPS 2022 Spotlight).

P2P is a framework to leverage pre-trained image models for 3D analysis. We transform point clouds into colorful images with newly proposed geometry-preserved projection and geometry-aware coloring to adapt to pre-trained image models, whose weights are kept frozen during the end-to-end optimization of point cloud understanding tasks.

[arXiv][Project Page][Models]

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 p2p python=3.9
conda activate p2p
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
pip install opencv-python
pip install scikit-learn, h5py
conda install pyyaml, tqdm

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:

  P2P/
  |-- 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/
  

Usage

Train

bash tool/train.sh EXP_NAME CONFIG_PATH DATASET

For example, to train P2P model with ConvNeXt-B-1k as base model on the ModelNet40 dataset:

bash tool/train.sh p2p_ConvNeXt-B-1k config/ModelNet40/p2p_ConvNeXt-B-1k.yaml ModelNet40

Test

bash tool/test.sh EXP_NAME CONFIG_PATH DATASET

For example, to test P2P model with ConvNeXt-B-1k as base model the ModelNet40 dataset:

bash tool/test.sh p2p_ConvNeXt-B-1k config/ModelNet40/p2p_ConvNeXt-B-1k.yaml ModelNet40

Reproduce

bash tool/reproduce.sh DATASET MODEL

For example, to reproduce results of P2P model with ConvNeXt-B-1k as base model on the ModelNet40 dataset with our provided checkpoint:

bash tool/reproduce.sh ModelNet40 ConvNeXt-B-1k

Results

Quantitative Results

We provide pretrained P2P models:

To train our P2P framework with HorNet-L-22k as the image model, please download the pre-trained weight from the official HorNet repo. Please organize the directory as the following structure:

    P2P/
    |-- pretrained/
        |-- hornet_large_gf_in22k.pth
        |-- reproduce/
            |-- ckpt/
                |-- ModelNet40/
                    |-- ConvNeXt-B-1k-ModelNet40.pth
                |-- ScanObjectNN/
                    |-- ConvNeXt-B-1k-ScanObjectNN.pth

Visualization Results

Citation

If you find our work useful in your research, please consider citing:

@article{wang2022p2p,
title={P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Prompting},
author={Wang, Ziyi and Yu, Xumin and Rao, Yongming and Zhou, Jie and Lu, Jiwen},
journal={arXiv preprint arXiv:2208.02812},
year={2022}
}

Acknowledgements

Our code is inspired by BPNet, Point-BERT.