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nPSR

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Repository for the code of "PoissonNet: Resolution-Agnostic 3D Shape Reconstruction using Fourier Neural Operators"

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# Neural Poisson Surface Reconstruction (nPSR)

### [**Paper**](https://arxiv.org/abs/2308.01766) | [**Project Page**](https://juliushege.com/npsr/)

![](https://raw.githubusercontent.com/arsenal9971/nPSR/main/media/fcf9e348c54012082296d88107d065f6_250K.png)

This repository contains the implementation of the paper:

Neural Poisson Surface Reconstruction (nPSR): Resolution-Agnostic Shape Reconstruction from Point Clouds

We are currently working on a cleaned-up version of this code that includes more documentation and pre-trained weights. If you find our code or paper useful, please consider citing
```bibtex
@article{andrade2023npsr,
  title={Neural Poisson Surface Reconstruction: Resolution-Agnostic Shape Reconstruction from Point Clouds},
  author={Hector Andrade-Loarca, Julius Hege, Daniel Cremers, Gitta Kutyniok},
  journal={arXiv:2308.01766},
  year={2023}
}
```

## Installation

You need to first install all the dependencies. For that you can use [anaconda](https://www.anaconda.com/).

You can create an anaconda environment called `npsr` using
```
conda env create -f environment.yaml
conda activate npsr
```

## Training - Quick Start

First, download the data from: [ShapeNetsem dataset](https://shapenet.org/download/shapenetsem) and place the binvox files inside `./data`. Then run the script `scripts/generate_training_data_shapenet.py` to generate the training data, modifying the corresponding paths. After the training data has been generated one can simply train the model by running `scritps/train_shapenet.py`.

## Inference

Use the notebook `examples/Inference.ipynb` to evaluate the model on different shapes. You can download our pretrained weights from this [link](https://drive.google.com/drive/folders/1mJqQeDr0lkieeYQdT2WNTBTHgtSdIEa-?usp=sharing).

nPSR

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