LOD3 buildings (Tested on Ubuntu 18.04lts)

This repository contains the codes for computing geometrical digital twins as LOD3 models for buildings using a structure from motion and semantic segmentation. The methodology hereby implements was presented in the paper [Generating LOD3 building models from structure-from-motion and semantic segmentation" by Pantoja-Rosero et., al. (2022)] (https://doi.org/10.1016/j.autcon.2022.104430)

How to use it? (Note: tested for ubuntu 18.04lts)

1. Clone repository

Clone repository in your local machine. All codes related with method are inside the src directory.

2. Download data and CNN weights

Example input data can be downloaded from Dataset for generating LOD3 building models from structure-from-motion and semantic segmentation. This datased contains 5 main folders. SfM, polyfit, weights, data and results. Extract the folders data/ and weights/ and place them inside the repository folder

2a. Repository directory

The repository directory should look as:

lod3_buildings
└───data
└───docs
└───examples
└───src
└───weights

3. Environment

Create a conda environment and install python packages. At the terminal in the repository location.

conda create -n lod3_buildings python=3.7

conda activate lod3_buildings

pip install -r requirements.txt

pip3 install torch torchvision

4. Third party software

The method needs as input Structure from Motion information and LOD2 model that are computed by Meshroom and Polyfit respectively. Please refeer to the links to know how to use their methodologies.

In addition to create the final 3D LOD3 building models, it is necessary FreeCAD python console and its methods. You can either download the appimage and extract their content as freecad_dev or download the folder here freecad_dev. Place the folder freecad_dev in the repository location. The repository directory should look as:

lod3_buildings
└───data
└───docs
└───examples
└───freecad_dev
  └───usr
    └───bin
    └───...
└───src
└───weights

5. Testing method with pusblished examples

Inside the folder examples/ we have provide the input scripts that our algorithm needs. Two input scripts are necessary: ..._main_op.py and ..._main_LOD3.py. To run for instance the example p2_LOD3_00_School simply open the terminal inside the src folder (with the environment activated) and write the next command:

./LOD3.sh ../examples/p2_LOD3_00_School_main_op.py ../examples/p2_LOD3_00_School_main_LOD3.py

The algorithm first will create the openings in 3D and then merge them with the LOD2 model. Run the other examples similarly to the previous inline command.

6. Creating your own digital twin as LOD3 model

Create a folder your_LOD3_data_folder inside the data\ folder. Inside your_LOD3_data_folder create extra folders with the next structure:

lod3_buildings
└───data
  └───your_LOD3_data_folder
    └───cameras
    └───images
      └───im1
      └───im2
    └───keypoints
    └───polyfit      
...

The methodology requires as input the next:

• cameras.sfm: file containing the poses information (camera position - location, rotation). After running the default Meshroom pipeline, this file is output in the folder MeshroomCache/StructureFromMotion/13231fdsf/. Copy that file inside the your_LOD3_data_folder\cameras
• two registered views images for each facade containing the openings: For each facade, place one image in which the openings are visible in the folder data/your_LOD3_data_folder/images/im1/ and othere image view in the folder data/your_LOD3_data_folder/images/im2/.
• polyfit.obj: use Polyfit pipeline either with the sparse or dense point cloud produced by the Meshroom pipeline. Note that it might be necessary to pre-process the point clouds deleting noisy points before running Polyfit. Save the output file as polyfit.obj or polyfit_dense.obj and place it in the folder data/your_LOD3_data_folder/polyfit/

Check the files of the data examples provided if neccessary to create the inpute data.

Finally create the two input scripts (your_LOD3_main_op.py and your_LOD3_main_LOD3.py) following the contents the given examples. Open the terminal inside the src folder (with the environment activated) and write the next command:

./LOD3.sh ../examples/your_LOD3_main_op.py ../examples/your_LOD3_main_LOD3.py

7. Results

The results will be saved inside results folder. Images of the pipeline stages are saved together with .obj files for the openings in 3D and the LOD3 model.

7.a Final repository directory

The repository directory after runing the medothology looks as:

lod3_buildings
└───data
└───docs
└───examples
└───freecad_dev
└───results
└───src
└───weights

8. Citation

We kindly ask you to cite us if you use this project, dataset or article as reference.

Paper:

@article{Pantoja-Rosero2022b,
title = {Generating LOD3 building models from structure-from-motion and semantic segmentation},
journal = {Automation in Construction},
volume = {141},
pages = {104430},
year = {2022},
issn = {0926-5805},
doi = {https://doi.org/10.1016/j.autcon.2022.104430},
url = {https://www.sciencedirect.com/science/article/pii/S092658052200303X},
author = {B.G. Pantoja-Rosero and R. Achanta and M. Kozinski and P. Fua and F. Perez-Cruz and K. Beyer},
}

Dataset:

@dataset{Pantoja-Rosero2022b-ds,
  author       = {Pantoja-Rosero, Bryan German and
                  Achanta, Radhakrishna and
                  Kozinski, Mateusz and
                  Fua, Pascal and
                  Perez-Cruz, Fernando and
                  Beyer, Katrin},
  title        = {{Dataset for generating LOD3 building models from 
                   structure-from-motion and semantic segmentation}},
  month        = jun,
  year         = 2022,
  publisher    = {Zenodo},
  version      = {v.0.0},
  doi          = {10.5281/zenodo.6651663},
  url          = {https://doi.org/10.5281/zenodo.6651663}
}