scAnt - Open Source 3D Scanner

scAnt is a completely open source and low-cost macro 3D scanner, designed to digitise insects of a vast size range and in full colour. Aiming to automate the capturing process of specimens as much as possible, the project comes complete with example configurations for the scanning process, as well as additional scripts including stacking and complex masking of images to prepare them for various photogrammetry software of your choice! For a look at a few scAnt results check-out http://bit.ly/ScAnt-3D as well as our Sketchfab Collection!

All structural components of the scanner can be manufactured using 3D-printing and laser cutting and are available for download as .ipt, .iam, .stl, and .svg files from our thingiverse page.

Installation

scAnt is supported by 64 bit versions of Windows 10 and Ubuntu 18.04. Newer releases of Ubuntu will likely not pose an issue but only these configurations have been tested so far. The pipeline and GUI have been designed specifically for use with FLIR Blackfly cameras, and Pololu USB Stepper drivers to limit the number of required components for the scanner as much as possible. We are planning on including support for other cameras and stepper drivers in the future as well. Please refer to our thingiverse page for a full list of components.

The easiest way to get your scanner up and running is by installing our pre-configured anaconda environment.

cd conda_environment
conda env create -f scAnt_UBUNTU.yml

After the environment has been created successfully, close the terminal and open a new one. Run the following line to activate your new environment and continue the installation.

conda activate scAnt

If you instead prefer to use other package managers or want to integrate the scanner into an existing environment, here is a list of package requirements:

python >= 3.6
pip
numpy
matplotlib
opencv >= 4.1.0
pyqt 5
imutils
pillow
scikit-image

Additionally, drivers and libraries for the used camera and stepper drivers need to be installed, specific to your system.

Ubuntu 18.04

Download the drivers and python bindings for Spinnaker & Pyspin from the official FLIR page:

meta.box.lenovo.com

FLIR Support / Spinnaker / Linux Ubuntu / Ubuntu 18.04

download the tar.gz file for your architecture (usually amd64)

FLIR Support / Spinnaker / Linux Ubuntu / Python / Ubuntu 18.04 / x64

depending on your python version, download the respective file. For our conda environment download ...cp37-cp37m_linux_x86_64.tar.gz

Unpack all files in a folder of your choice before you continue

Install all required dependencies before installing FLIR's Spinnaker software

sudo apt-get install libavcodec57 libavformat57 libswscale4 libswresample2 libavutil55 libusb-1.0-0 libgtkmm-2.4-dev

Once all dependencies are installed, install spinnaker from its extracted folder

sudo sh install_spinnaker.sh

during installation ensure to add your user to the user-group and accept increasing allocated USB-FS memory size to 1000 MB in order to increase the video stream buffer size
Reboot your computer to make the installation become effective
launch spinview and connect your FLIR camera to verify your installation (if the application is already launched when plugging in your camera, refresh the list in case it is not listed right away)
now install the downloaded .whl file for your python environment. Ensure you activate your python environment before running the pip install command below.

pip install spinnaker_python-1.x.x.x-cp37-cp37m-linux_x86_64.whl

To verify everything has to be installed correctly run Live_view_FLIR.py from the GUI folder.

cd scant/GUI
python Live_view_FLIR.py

If a live preview of the camera appears for a few seconds and an example image is saved (within the GUI folder), all camera drivers and libraries have been installed correctly!

Stepper driver setup

The Pololu stepper drivers can be controlled and set up via console based commands. Simply download the drivers specific to your system from pololu.com Additional information regarding installation and a list of supported commands can also be found there. As all drivers used are open source as well, their code can be found on Pololu's Git as well.
Simply unpack the downloaded .tar.xy file and install the driver by:

sudo pololu-tic-*/install.sh

Afterwards, reboot your computer to update your user privileges automatically, otherwise you will have to use sudo to access your USB stepper drivers.
If one or all of the stepper controllers were previously plugged into your computer unplug them and then plug them back in, so they are recognised correctly by your computer. Now, open the terminal and run the following command:

ticcmd –list

This should output a list of all connected USB stepper drivers.

Now that your camera and steppers are all set up, you can run a complete functionality check of the scanner by running the Scanner_Controller.py script.

cd scAnt/scripts
python Scanner_Controller.py

the scanner will then home all axes, drive to a set of example positions and capture images as it would during scanning for a very coarse grid.
If no errors appear, images will be saved and “Demo completed successfully” is printed to the console

Image Processing

A number of open source tools are used for processing the RAW images captured by the scanner. For a detailed explanation of each, refer to the official hugin and exiftool documentation. The following lines will install all that good stuff:

sudo add-apt-repository ppa:hugin/hugin-builds
sudo apt-get update
sudo apt-get install hugin enblend
sudo apt install hugin-tools
sudo apt install enfuse
sudo apt install libimage-exiftool-perl

Windows installation guide coming soon

Meshroom Guide

Add your camera to the sensor database

Within the directory of the downloaded Meshroom installation go to the following folder and edit the file “cameraSensors.db” using any common text editor:

…/Meshroom-2019.2.0/AliceVision/share/AliceVision/cameraSensors.db

The entry should contain the following:

Make;Model;SensorWidth

Ensure to enter these details as they are listed in your project configuration file, thus, metadata of your stacked and masked images. There should be no spaces between the entries. For our configuration add the following line:

FLIR;BFS-U3-200S6C-C;13.1

Adding the correct sensor width is crucial in computing the camera intrinsics, such as distortion parameters, object scale, and distances. Otherwise the camera alignment, during feature matching and structure-from-motion steps are likely to fail.

Once these details have been added, launch Meshroom and drag your images named …cutout.tif into Meshroom. If the metadata and added camera sensor are recognised, a green aperture icon should be displayed over all images.

If not all images are listed, or the aperture icon remains red / yellow, execute the helper script “batch_fix_meta_data.py” to fix any issues resulting from your images' exif files.

Setting up the reconstruction pipeline

Try to run the pipeline with this configuration, before attempting to use approximated camera positions. Only if issues with the alignment of multiple camera poses arise, you should use approximated positions, as fine differences in the scanner setup can cause poorer reconstruction results, without guided matching (available only in the 2020 version of Meshroom)!

CameraInit

No parameters need to be changed here.
However, ensure that only one element is listed under Intrinsics. If there is more than one, remove all images you imported previously, delete all elements listed under Intrinsics, and load your images again. If the issue persists, execute the helper script “batch_fix_meta_data.py” to fix any issues resulting from your images exif files.

FeatureExtraction

Enable Advanced Attributes** by clicking on the three dots at the upper right corner.
Describer Types: Check sift and akaze
Describer Preset: Normal (pick High if your subject has many fine structures)
Force CPU Extraction: Uncheck

ImageMatching

Max Descriptors: 10000
Nb Matches: 200

FeatureMatching

Describer Types: Check sift and akaze
Guided Matching: Check

StructureFromMotion

Describer Types: Check sift and akaze
Local Bundle Adjustment: Check
Maximum Number of Matches: 0 (ensures all matches are retained)

PrepareDenseScene

No parameters need to be changed here.

DepthMap

Downscale: 1 (use maximum resolution of each image to compute depth maps)

DepthMapFilter

Min View Angle: 1
Compute Normal Maps: Check

Meshing

Estimate Space from SfM: Uncheck (while this will potentially produce “floaters” that need to be removed during post processing it assists in reserving very fine / long structures, such as antennae)
Min Observations for SfM Space Estimation: 2 (only required if above attribute remains checked)
Min Observations Angle for SfM Space Estimation: 5 (only required if above attribute remains checked)
Max Input Points: 100000000
simGaussianSizeInit: 5
simGaussianSize: 5
Add landmarks to the Dense Point Cloud: Check

MeshFiltering

Filter Large Triangles Factor: 40
Smoothing Iterations: 2

Texturing

Texture Side: 16384
Unwrap Method: LSCM (will lead to larger texture files, but much higher surface quality)
Texture File Type: png
Fill Holes: Check

Now click on start and watch the magic happen. Actually, this is the best time to grab a cup of coffee, as the reconstruction process takes between 3 and 10 hours, depending on your step size, camera resolution, and system specs.

Exporting the textured mesh:

All outputs within Meshroom are automatically saved in the project’s environment. By right clicking on the Texturing node and choosing “Open Folder” the location of the created mesh (.obj file) is shown.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

License

scAnt - Open Source 3D Scanner and Processing Pipeline

Thriceguy / scAnt

scAnt - Open Source 3D Scanner

Installation

Meshroom Guide

Contributing

License

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