【Code of CVPR 2022 paper】
Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation
Paper address: https://arxiv.org/abs/2204.08196
Pytorch 1.9.0
CUDA 10.2
Download the pretrained models from the link and unzip it to ./out/
https://pan.baidu.com/s/1OPVnCHq129DBMWh5BA2Whg
access code: hgii
or
https://drive.google.com/file/d/12TifjDW2L7r2LK3AGDgabHdN3f0xY5DR/view?usp=share_link
Run the following command for compiling dense.cpp which generates dense seed points. Note that the size of the input point cloud is currently limited to 5000, if you want to change this limit, you need to modify the size of the kdtree.
g++ -std=c++11 dense.cpp -O2 -o dense
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
Download the training dataset from the link and unzip it to the working directory
https://pan.baidu.com/s/1VQ-3RFO02fQfcLBfqvCBZA
access code: vpfm
or
https://1drv.ms/f/s!AsP2NtMX-kUTml4U3DYUD6Hy9FJn?e=8QfJTH
Then run the following commands for training our network
python trainfn.py
python trainfd.py
We present a fast implementation for building the dataset, which is based on occupancy_networks.
Follow the link occupancy_networks to obtain pointclouds and watertight meshes, notice that we only use ShapeNet dataset v1.
Then move scripts to occupancy_networks/scripts run the following commands for building traindata for fd and fn:
bash dataset_shapenet/build-fd.sh
bash dataset_shapenet/build-fn.sh
bash dataset_shapenet/installfd.sh
bash dataset_shapenet/installfn.sh
The code for evaluation can be download from:
https://github.com/pleaseconnectwifi/Meta-PU/tree/master/evaluation_code
https://github.com/jialancong/3D_Processing
If the code is useful for your research, please consider citing:
@inproceedings{sapcu,
title = {Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation},
author = {Wenbo Zhao, Xianming Liu, Zhiwei Zhong, Junjun Jian, Wei Gao, Ge Li, Xiangyang Ji},
booktitle = {Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {1999-2007}
}
The code is based on occupancy_networks and DGCNN, If you use any of this code, please make sure to cite these works.