Skip to content

Latest commit

 

History

History
58 lines (39 loc) · 3.12 KB

README.md

File metadata and controls

58 lines (39 loc) · 3.12 KB
Raw

Aerial Image-based Inter-day Registration for Precision Agriculture

If you use this code in your academic work, please cite (PDF):

@inproceedings{gao2024interday,
  title={Aerial Image-based Inter-day Registration for Precision Agriculture},
  author={Gao, Chen and Daxinger, Franz and Roth, Lukas and Maffra, Fabiola and Beardsley, Paul and Chli, Margarita and Teixeira, Lucas},
  booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)},
  year={2024}
}

Video

Mesh

Repo structure

This repo includes code for two tasks: crop alignment and train LoFTR for crop images. For clarity, we gather the relevant codes in different folders.

  • dataset: The dataset (except images and depth images) needed to excute the registration pipeline and train LoFTR. Scripts to download addition data, and prepare the datasets for training LoFTR are also included.

  • crop_alignment: The pipeline to compute 3D models of a crop field and to properly register them. Includes the codes of the experiments done in the paper. This relies on the repository Hierarchical_Localization for performing localization between query and reference images.

  • LoFTR: Code for training LoFTR. Modified to account for height variations between days. The code is based on the implementation of original LoFTR.

Please go to each subdirectory for further information.

Installations

Retrieve the repo ans submodules first. Then please refer to the instructions of each individual tasks.

git clone --recursive https://github.com/VIS4ROB-lab/interday_crop_registration/

# This repository includes external local features as git submodules, don't forget to pull submodules 
cd interday_crop_registration
git submodule update --init --recursive`

For crop alignment, please follow the steps here. You also need this environment to run the scripts in the dataset folder.

For LoFTR, please follow the steps here.

Download datasets

The dataset folder in this repo is only a skeleton. It does not contain original images or associated depth images. Before starting to run certain tasks, you need to download the corresponding data.

Run the script with the following command.

python ./dataset/dataset_downloader.py <destination_folder> <dataset>

Replace <destination_folder> with the path to the destination folder where you want to save the downloaded datasets.

Replace <dataset> with one of the following choices:

  • train: Downloads the images and depth images of the Wheat Dataset from 2018 to retrain LoFTR.
  • alignment: Downloads the images of the Wheat Dataset from 2019 to perform crop alignment.

How to run

Please refer to each subdirectory for further instructions.