aruco-estimator 1.1.5

Aruco Scale Factor Estimation

Automatic Aruco marker-based scale factor estimation (Work in Progress!)

About

This project aims to automatically compute the correct scale of a point cloud generated with COLMAP by placing an aruco marker into the scene.

Installation

PyPi

This repository is tested on Python 3.6+ and can be installed from PyPi

pip install aruco-estimator

Usage

Dataset

An exemplary data set is provided. The dataset shows a simple scene of a door with an aruco marker. Other dataset might follow in future work. It can be downloaded by using

from aruco_estimator import download

dataset = download.Dataset()
dataset.download_door_dataset(output_path='.')

API

A use of the code on the provided dataset can be seen in the following block. The most important function is aruco_scale_factor.run(). Here, an aruco marker is searched for in each image. If a marker is found in at least 2 images, the position of the aruco corner in 3D is calculated based on the camera poses and the corners of the aruco maker.Based on the positions of the corners of the square aruco marker, the size of the marker in the unscaled reconstruction can be determined. With the correct metric size of the marker, the scene can be scaled true to scale using aruco_scale_factor.apply(true_scale).

from aruco_estimator.aruco_scale_factor import ArucoScaleFactor
from aruco_estimator import download
import os
import open3d as o3d

# Download example dataset. Door dataset is roughly 200 MB
dataset = download.Dataset()
dataset.download_door_dataset()

# Init & run pose estimation of corners in 3D & estimate mean L2 distance between the four aruco corners
aruco_scale_factor = ArucoScaleFactor(project_path=dataset.dataset_path)
aruco_distance = aruco_scale_factor.run()
print('Size of the unscaled aruco markers: ', aruco_distance)

# Calculate scaling factor, apply it to the scene and save scaled point cloud
dense, scale_factor = aruco_scale_factor.apply(true_scale=dataset.scale)  # scale in cm
print('Point cloud and poses are scaled by: ', scale_factor)
print('Size of the scaled (true to scale) aruco markers in meters: ', aruco_distance * scale_factor)

# Visualization of the scene and rays BEFORE scaling. This might be necessary for debugging
aruco_scale_factor.visualize_estimation(frustum_scale=0.4)
o3d.io.write_point_cloud(os.path.join(dataset.colmap_project, 'scaled.ply'), dense)
aruco_scale_factor.write_data()

Source

If you want to install the repo from source make sure that conda is installed. Afterwards clone this repository, give the bash file executable rights and install the conda env:

git clone https://github.com/meyerls/aruco-estimator.git
cd aruco-estimator
chmod u+x init_env.sh
./init_env.sh

Finally install all python dependencies in the activated conda environment via

pip install -r requirements.txt

Usage of Command Line

usage: scale_estimator.py [-h] [--colmap_project COLMAP_PROJECT] [--dense_model DENSE_MODEL] [--aruco_size ARUCO_SIZE] [--visualize] [--point_size POINT_SIZE] [--frustum_size FRUSTUM_SIZE] [--test_data]

Estimate scale factor for COLMAP projects with aruco markers.

optional arguments:
-h, --help                             show this help message and exit
--colmap_project COLMAP_PROJECT        Path to COLMAP project
--dense_model DENSE_MODEL              name to the dense model
--aruco_size ARUCO_SIZE                Size of the aruco marker in cm.
--visualize                            Flag to enable visualization
--point_size POINT_SIZE                Point size of the visualized dense point cloud. Depending on the number of points in the model. Between 0.001 and 2
--frustum_size FRUSTUM_SIZE            Size of the visualized camera frustums. Between 0 (small) and 1 (large)
--test_data                            Download and try out test data

To test the code on your local machine try the example project by using:

python scale_estimator.py --test_data

Limitation / Improvements

• Up to now only SIMPLE_RADIAL and PINHOLE camera models are supported. Extend all models
• Install CLI Tool vi PyPi
• Up to now only one aruco marker per scene can be detected. Multiple aruco marker could improve the scale estimation
• Different aruco marker settings and marker types should be investigated for different scenarios to make it either more robust to false detections
• Geo referencing of aruco markers with earth coordinate system using GPS or RTK
• Only COLMAP is supported. Add additional reconstruction software.

Acknowledgement

• The Code to read out the binary COLMAP data is partly borrowed from the repo COLMAP Utility Scripts by uzh-rpg.
• The visualization of the wooden block is created from the dataset found in [1]

Trouble Shooting

• In some cases cv2 does not detect the aruco marker module. Reinstalling opencv-python and opencv-python-python might help Source

References

[1] Erich, F., Bourreau, B., Neural Scanning: Rendering and determining geometry of household objects using Neural Radiance Fields Link. 2022

Citation

Please cite this paper, if this work helps you with your research:

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  author="H",
  title="",
  booktitle="",
  year="",
  pages="",
  isbn=""
}