Code for CVPR2022 Paper
The code is for the CVPR 2022 paper: Neural Points: Point Cloud Representation with Neural Fields for Arbitrary Upsampling.
The code has been tested 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
How to use the code:
Download our dataset: dataset, (extracting code: qiqq). Put the ‘Sketchfab2’ folder into: [ProjectPath]/data.
Firstly, you need to change the working directory:
To obtain the testing results of the testing set, run:
To train our network, run: