PUDM

This repo is the official project repository of the paper A Conditional Denoising Diffusion Probabilistic Model for Point Cloud Upsampling.

• [ paper ] [ supp ]

The Version Update Notification

We plan to release PUDM V2 in the future.
This is expected to result in the following improvements compared to PUDM:

1. Decoupled the point number. PUDM V2 is based on the points number, not the upsampling ratio. For example, the output (S,3), S can be any positive integer. This means that PUDM V2 is also able to perform downsampling.
   
2. Higher quality surface generation. This focuses more on the qualitative results (visualization) rather than the quantitative ones (CD or HD).

The Overall Framework

Overview

• Citation
• Installation
• Data Preparation
• Model Zoo
• Quick Start

Citation

If you find PUDM useful to your research, please cite our work as an acknowledgment.

@InProceedings{Qu_2024_CVPR,
    author    = {Qu, Wentao and Shao, Yuantian and Meng, Lingwu and Huang, Xiaoshui and Xiao, Liang},
    title     = {A Conditional Denoising Diffusion Probabilistic Model for Point Cloud Upsampling},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2024},
    pages     = {20786-20795}
}

Installation

Requirements

The following environment is recommended for running PUDM (an NVIDIA 3090 GPU):

• Ubuntu: 18.04 and above
• CUDA: 11.1 and above
• PyTorch: 1.9.1 and above
• python: 3.7 and above

Environment

• Base environment

conda create -n pudm python=3.7 -y
conda activate pudm

conda install cudatoolkit
pip install nvidia-cudnn-cu11

pip install torch==1.9.1+cu111 torchvision==0.10.1+cu111 torchaudio==0.9.1 -f https://download.pytorch.org/whl/torch_stable.html

pip install open3d termcolor tqdm einops transforms3d==0.3.1
pip install msgpack-numpy lmdb h5py hydra-core==0.11.3 pytorch-lightning==0.7.1
pip install scikit-image black usort flake8 matplotlib jupyter imageio fvcore plotly opencv-python

# For installing pytorch3d, please follow:
1. download pytorch3d-0.6.1-py37_cu111_pyt191.tar.bz2 from https://anaconda.org/pytorch3d/pytorch3d/files?page=10
2. conda install pytorch3d-0.6.1-py37_cu111_pyt191.tar.bz2

# compile C++ extension packages
sh compile.sh

Data Preparation

Please download [ PU1K ] and [ PUGAN ].

# For generating test data, please see **PUDM-main/pointnet2/dataloder/prepare_dataset.py**
cd PUDM-main/pointnet2/dataloder

# For example 1, we can generate 4x test set of PUGAN:
python prepare_dataset.py --input_pts_num 2048 --R 4 --mesh_dir mesh_dir --save_dir save_dir

# For example 2, we can generate 4x test set of PUGAN with 0.1 Gaussion noise:
python prepare_dataset.py --input_pts_num 2048 --R 4 --noise_level 0.1 --noise_type gaussian --mesh_dir mesh_dir --save_dir save_dir

Model Zoo

Please download our checkpoints:
[ Baidu Netdisk ] (code : r2h9) or [ Google Drive ]
Please put checkpoints in the PUDM-main/pointnet2/pkls folder.
*Released model weights are temporarily as the model structure of PUDM may be adjusted later.

Quick Start

Example

We provide some examples. These examples are in the PUDM-main/pointnet2/example folder. The results are in the PUDM-main/pointnet2/test/example folder.

# For example, we can run 30 steps (DDIM) to generate 4x point cloud on KITTI with the pre-trained model of PUGAN.
# We provide the function (bin2xyz) of converting *.bin to *.xyz in **PUDM-main/pointnet2/dataloder/dataset_utils.py**.
cd PUDM-main/pointnet2
python example_samples.py --dataset PUGAN --R 4 --step 30 --example_file ./example/KITTI.xyz

This will produce the following result:

Training

We provide two datasets to train PUDM, PUGAN and PU1K. The results are in the PUDM-main/pointnet2/exp_{dataset} folder.

# For training PUGAN
cd PUDM-main/pointnet2
python train.py --dataset PUGAN

# For training PU1K
cd PUDM-main/pointnet2
python train.py --dataset PU1K

Testing

We provide two datasets to test PUDM. The results are in the PUDM-main/pointnet2/test/{dataset} folder.

# For testing PUGAN
cd PUDM-main/pointnet2
python samples.py --dataset PUGAN --R 4 --step 30 --batch_size 27

# For testing PU1K
cd PUDM-main/pointnet2
python samples.py --dataset PU1K --R 4 --step 30 --batch_size 43