gabzech / sat2pc

Sat2PC

By Yoones Rezaei, Stephen Lee

Citation

If you find our paper helpful in your work, please consider citing:

@misc{https://doi.org/10.48550/arxiv.2205.12464,
  doi = {10.48550/ARXIV.2205.12464},

  url = {https://arxiv.org/abs/2205.12464},

  author = {Rezaei, Yoones and Lee, Stephen},

  keywords = {Computer Vision and Pattern Recognition (cs.CV), Artificial Intelligence (cs.AI), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},

  title = {sat2pc: Estimating Point Cloud of Building Roofs from 2D Satellite Images},

  publisher = {arXiv},

  year = {2022},

  copyright = {arXiv.org perpetual, non-exclusive license}
}

Introduction

In this repository we release the code and data for our paper "sat2pc: Generating Building Roof's Point Cloud from a Single 2D Satellite Images" in ICCPS 2023. You can find the original paper here.

Installation

To use this repository you need an environemnt with python 3.7.9. We suggest creating a conda environment with the following command:

conda env create -f environment.yml

Next, activate the environment:

conda activate sat2pc

Finally, cd into the repository folder and run the following commands one after another:

conda install -c "nvidia/label/cuda-11.7.0" cuda
cd PyTorchEMD
python setup.py install
cd ../neuralnet-pytorch-master
python setup.py install

These commands will install extra required packages.

Data

The dataset from the paper can be dowloaded from here.

Usage

To train the model you can run the following command:

python train.py --config ./configs/sat2pc.gin --data-dir ./datasets/

To test the model you can run the following command:

python test.py --config ./configs/sat2pc.gin --data-dir ./datasets/ --ckpt-path [location of the saved weights]

Visualization

To visualize the predictions from the model you can use the following command:

python visualize.py --data-dir ./datasets --results [location of the .res file generated by running the test script]