【Code of CVPR 2022 paper】Neural Points: Point Cloud Representation with Neural Fields for Arbitrary Upsampling (CVPR 2022).
- Paper address: https://arxiv.org/abs/2112.04148
- Project webpage: https://wanquanf.github.io/NeuralPoints.html
The code has been tested on Ubuntu 18, with Python3.8, PyTorch 1.6 and Cuda 10.2:
conda create --name NePs
conda activate NePs
conda install pytorch=1.6.0 torchvision=0.7.0 cudatoolkit=10.2 -c pytorch
conda install -c conda-forge igl
Before running the code, you need to build the cuda&C++ extensions of Pytorch:
cd [ProjectPath]/model/model_for_supp/pointnet2
python setup.py install
Download our dataset: dataset, (extracting code: qiqq). Put the 'Sketchfab2' folder into: [ProjectPath]/data.
Firstly, you need to change the working directory:
cd [ProjectPath]/model/conpu_v6
To obtain the testing results of the testing set, run:
python train_script101_test.py
To train our network, run:
python train_script101.py
Please cite this paper with the following bibtex:
@inproceedings{feng2022np,
author = {Wanquan Feng and Jin li and Hongrui Cai and Xiaonan Luo and Juyong Zhang},
title = {Neural Points: Point Cloud Representation with Neural Fields for Arbitrary Upsampling},
booktitle = {{IEEE/CVF} Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2022}
}
In this repo, we borrowed the backbone structure from DGCNN.
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