PU-SSAS

【Code of TPAMI paper】

Self-Supervised Arbitrary-Scale Implicit Point Clouds Upsampling

Paper address

Environment

Pytorch 1.9.0

CUDA 10.2

Evaluation

a. Download models

Download the pretrained models from the link and unzip it to ./out/

https://drive.google.com/file/d/1W0t-Ea6ucJDQUt2mS_fUnvYiiMjZBRt4/view?usp=sharing

b. Compilation

Run the following command for compiling dense.cpp which generates dense seed points

g++ -std=c++11 dense.cpp -O2 -o dense

c. Evaluation

You can now test our code on the provided point clouds in the test folder. To this end, simply run

python generate.py

The 4X upsampling results will be created in the testout folder.

Ground truth are provided by Meta-PU

Training

Download the training dataset from the link and unzip it to /data/

https://pan.baidu.com/s/1yaacibc50d0dIWcW7OIxEA 
access code:lmwf

or

https://1drv.ms/f/s!AsP2NtMX-kUTmw44ZfSvhV_PLzxu?e=Y8iL97

Then run the following commands for training our network

python trainfn.py
python trainfd.py

Generate Dataset

Download the pointclouds and watertight meshes from the link and unzip it to /data/

https://pan.baidu.com/s/1kWstsZMiZOJuGm5yvpNI3Q 
access code:208c

or

https://1drv.ms/f/s!AsP2NtMX-kUTmwxUMh-AZ5sJ7nl3?e=mHjhb9

Then run build.sh in /scripts/

If you want to generate the pointclouds and watertight meshes from other dataset, please follow the link: occupancy_networks#building-the-dataset

Evaluation Code

The code for evaluation can be download from:

https://github.com/pleaseconnectwifi/Meta-PU/tree/master/evaluation_code
https://github.com/jialancong/3D_Processing

Citation

If the code is useful for your research, please consider citing:

@ARTICLE{SelfPCU,
  author={Zhao, Wenbo and Liu, Xianming and Zhai, Deming and Jiang, Junjun and Ji, Xiangyang},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={Self-Supervised Arbitrary-Scale Implicit Point Clouds Upsampling}, 
  year={2023},
  volume={},
  number={},
  pages={1-13},
  doi={10.1109/TPAMI.2023.3287628}}

Acknowledgement

The code is based on occupancy_networks and DGCNN, If you use any of this code, please make sure to cite these works.