https://github.com/prbonn/plant_pcd_segmenter
High Precision Leaf Instance Segmentation for Phenotyping in Point Clouds Obtained Under Real Field Conditions
https://github.com/prbonn/plant_pcd_segmenter
Last synced: 6 months ago
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High Precision Leaf Instance Segmentation for Phenotyping in Point Clouds Obtained Under Real Field Conditions
- Host: GitHub
- URL: https://github.com/prbonn/plant_pcd_segmenter
- Owner: PRBonn
- Created: 2023-06-12T16:45:27.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-12-05T16:44:56.000Z (about 2 years ago)
- Last Synced: 2025-04-13T14:46:44.045Z (9 months ago)
- Language: Python
- Size: 521 KB
- Stars: 21
- Watchers: 4
- Forks: 2
- Open Issues: 1
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Metadata Files:
- Readme: README.md
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README
# High Precision Leaf Instance Segmentation for Phenotyping in Point Clouds Obtained Under Real Field Conditions
This repo contains the code for our publication "High Precision Leaf Instance Segmentation for Phenotyping
in Point Clouds Obtained Under Real Field Conditions". The paper can be downloaded **[here](https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/marks2023ral.pdf)**.
## Setup
- Install docker, docker-compose and nvidia-docker2 on your machine
- Clone this repo to a folder on your pc
- Edit ```docker-compose.yml``` to set ```/data``` and ```/logs``` paths. ```/data``` contains the dataset and ```/logs``` is the folder where the tensorboard logs will be saved. Edit the part of the paths before the ':' to make it point to the directories on your machine.
- Execute ```make build```
- Get a coffee and wait
## Train
- To train the leaf segmentation model with the provided config file run ```make train_instances CONFIG=config/leaf_segmentation.yaml```
## Test
- Execute ```make test_instances``` to evaluate the segmentation network
## Data
This approach has been tested on sugar beet and tree point clouds and has shown good performance.
The data used in our paper is not publicly available at the moment but will be in the future.
# Citation
If you use this repo, please cite us :
```
@article{marks2023ral,
author={Marks, Elias and Sodano, Matteo and Magistri, Federico and Wiesmann, Louis and Desai, Dhagash and Marcuzzi, Rodrigo and Behley, Jens and Stachniss, Cyrill},
journal={IEEE Robotics and Automation Letters},
title={High Precision Leaf Instance Segmentation for Phenotyping in Point Clouds Obtained Under Real Field Conditions},
year={2023},
volume={8},
number={8},
pages={4791-4798},
doi={10.1109/LRA.2023.3288383}}