Code for CVPR2022 Paper

The code is for the CVPR 2022 paper: Neural Points: Point Cloud Representation with Neural Fields for Arbitrary Upsampling.

Prerequisite Installation

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 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:

cd [ProjectPath]/model/conpu_v6

To obtain the testing results of the testing set, run:


To train our network, run:



View Github