Blender 3.x dramatically speeds up rendering among other enhancements. BlAInder Range Scanner needs just a minor change to be compatible with the latest versions.
This Blender add-on enables you to simulate lidar, sonar and time of flight scanners in your scene. Each point of the generated point cloud is labeled with the object or part id that was set before the simulation. The obtained data can be exported in various formats to use it in machnie learning tasks (see Examples section).
The paper can be found here: https://www.mdpi.com/1424-8220/21/6/2144
The figure shows a group of chairs (left), a Blender camera object and a light source (right). The chair legs and seats are randomly colored differently to make it easier to distinguish them in the following images.
In each of the four figures, a generated, three-dimensional point cloud can be seen. The different colors for each data point have different meanings:
- top left: original color from object material
- top right: grey scale intensity representation
- bottom left: each color stands for one object category (blue = floor, red = chair)
- bottom right: each color represents one object subcategory (blue = floor, red/green = seats, orange/turquoise = legs)
Note: the left and middle chairs have the same colors because the subobjects were classified identically (see Classification).
Supported formats:
In addition to the 3D points clouds, the add-on can also export 2D images.
- top left: the image rendered from Blenders engine
- top right: depthmap
- bottom left: segmented image
- bottom right: segmented image with bounding box annotations
Supported formats:
Installation
Dependencies
Usage (GUI)
Usage (command line)
Visualization
Examples
Scene generation
Development
About
License
It is recommended to use Blender 2.93 LTS. The add-on might work with Blender 3.x, but no support is currently given for specific problems with that version, although you can open an issue. The next LTS might be available with Blender 3.3 (see here) for which the add-on will be updated when available.
WARNING: DO NOT install the addon via both ways or the two versions are mixed up and cause errors.
- Clone the repository. This might take some time as the examples are quite large.
- Zip the
range_scannerfolder. - Inside Blender, go to
Edit->Preferences...->Add-ons->Install...and select the.zipfile.
- Clone the repository.
- Copy the
range_scannerfolder toBlender 2.93/2.93/scripts/addons_contrib.
The full installation of Blainder and all dependencies inside a fresh Blender copy can be done using the following commands on Ubuntu:
sudo apt-get update
sudo apt-get -y install git
wget https://download.blender.org/release/Blender2.93/blender-2.93.6-linux-x64.tar.xz
tar -xf blender-2.93.6-linux-x64.tar.xz
git clone https://github.com/ln-12/blainder-range-scanner.git
mkdir ./blender-2.93.6-linux-x64/2.93/scripts/addons_contrib/
cp -r ./blainder-range-scanner/range_scanner ./blender-2.93.6-linux-x64/2.93/scripts/addons_contrib/
./blender-2.93.6-linux-x64/2.93/python/bin/python3.9 -m ensurepip
./blender-2.93.6-linux-x64/2.93/python/bin/python3.9 -m pip install -r ./blainder-range-scanner/range_scanner/requirements.txt
./blender-2.93.6-linux-x64/blender
To use this add-on, you need to install
This add-on makes use of the following projects:
- blender-python-examples, licensed under GPLv3
After installing the add-on, you see the panel shown below to install the missing dependencies. You might need administrative priviledges to perfom this action (more info).
Open a terminal (as admin on Windows) and navigate into blainder-range-scanner/range_scanner. Then run on of the following commands depending on your system.
Windows
"C:\Program Files\Blender Foundation\Blender 2.93\2.93\python\bin\python.exe" -m pip install -r requirements.txt
WARNING: Make sure that the packages are installed inside C:\program files\blender foundation\blender 2.93\2.93\python\lib\site-packages, not C:\users\USER\appdata\roaming\python\python39\site-packages\ or Blender won't find them!
macOS
/Applications/Blender.app/Contents/Resources/2.93/python/bin/python3.9m -m pip install -r requirements.txt
In Blenders 3D View, open the sidebar on the right (click on the little <) and select Scanner.
If necessary, install the required dependencies (see Automatic installation).
Please note that not all of the following options are available for all scanner types.
Select the object in the scene which should act as range sensor. This object must be of type camera.
If enabled, all static meshes in the scene are joined into one mesh prior to the simulation.
This operator starts the actual scanning process. You should set all parameters (see the following sections) before you hit the button. It is generally recommended to open the command window to see any warning or errors occuring during simulation.
In this section you can select a predefined sensor. First, choose one of the categories Lidar, Sonar or Time of flight. Then you can select a specific sensor.
When pressing Load preset, all parameters are applied automatically.
The scanner type lets you define the operation mode of the sensor. Depending on the selected type, you can further specify the characteristics of the sensor.
The fields of view define the area which is covered by the scan horizontally and vertically. The resolution indicates the angle between to measurements.
In case of rotating sensors, the number of rotations per seconds is used to simulate correct measurements during animations.
In the case of the sideScan scanner type, you can set additional parameters (more info) and define the water profile for this scene.
The water surface level defines the z coordinate in your scene which is refered as a water depth of 0 meters. In the table below, you can fill in values for different water layers. Keep in mind to always start with a layer at 0m depth. This approach is used to quickly adjust the water level without the need to move the whole scene.
Example: you set a water surface level of z = 10 and add three layers at a depth of 0m, 3m and 6m. This means there is a layer between 0-3 m, another one between 3-6 m and a last layers which starts at 6 m depth and is infinitely deep (until it hits the bottom). Related to the scene's z coordinate, this means that you have borders between the layers at z = 7 and z = 4.
The minimum reflectivity needed to capture a reflected ray is approximated by the following model. At a distance of dmin a reflectivity of rmin is needed, while at dmax the reflectivity needs to be greater than rmax . Measurement below dmin are captured as long as the reflectivity is >0. For distances above dmax , no values are registered.
The following panel lets you define the parameters.
You can set the minimum and maximum reflectivity for the scene's targets at given distances.
The maximum reflection depth defines how often a ray can be reflected on surfaces before it gets discarded.
The reflectivity is defined by the material:
Diffuse material can be defined by changing the Base Color attribute of the Princinpled BSDF shader. The reflectivity is taken from the alpha parameter of a materials color.
To use a texture, add an Image texture node and link it to the input of Base Color.
To model glass objects, simply use the Glass BSDF shade and set the correct index of refraction with the IOR attribute.
To simulate a fully reflecting surface, you can set the Metallic attribute of the Princinpled BSDF shader to 1.0.
Objects can beclassified in the two following ways:
Select an object to add a custom property categoryID to set the main category (here: chair) and partID to set the subcategory (here: legs/plate). If no categoryID is provided, the object name is used as the category name instead. If no partID is given, the material name is used (see below).
The main category has to be set like explained above via categoryID. To differentiante parts within a single object, you can select the faces in edit mode and assign a specific material (here: leg/plate). Each subobject with the same material is treated as one category, even if they belong to different objects.
The settings in this panel correspond to the values inside Blenders Output Properties tab. You can define the range of frames, the number of skipped frames in each animation step and the number of frames per second (relevant for rotating scanners). Any techniques inside Blender to simulate motion and physics can be applied.
The constant offsets are applied to each measurement without any variation. You can choose between an absolute offset which is the same for each distance or a relative offset as percentage of the distance.
To simulate random errors during the measurement, you can specify the distribution with the given parameters.
To simulate rain, just set the amount of rain in millimeters per hour (see this paper).
For dust simulation, you can set the parameters to define a dust cloud starting at a given distance and with a given length (see this paper).
If this setting is enabled, the generated point cloud is added the to Blender scene.
This add-on can output the generated point clouds as .hdf5, .csv and .las files.
The option Export single frames defines if each animation frame should be exported in a separat file or if all steps are exported into a single file.
In the case of time of flight sensors, you can furthermore export the rendered image along with a segemented image (including pascal voc object descriptions) and a depthmap. You can specify the value range for the depthmap. All depth values at the minimum are white, whereas values at or above the maximum value appear black. Color values in-between are linearly interpolated.
These options are only meant for debugging the add-on. Use them with caution as adding output/line to the process can lead to significant perfomance issues!
When the code is located inside the addons_contrib directory (see script usage), you can use the scanner function via script the following way:
import bpy
import range_scanner
# Kinect
range_scanner.ui.user_interface.scan_static(
bpy.context,
scannerObject=bpy.context.scene.objects["Camera"],
resolutionX=100, fovX=60, resolutionY=100, fovY=60, resolutionPercentage=100,
reflectivityLower=0.0, distanceLower=0.0, reflectivityUpper=0.0, distanceUpper=99999.9, maxReflectionDepth=10,
enableAnimation=False, frameStart=1, frameEnd=1, frameStep=1, frameRate=1,
addNoise=False, noiseType='gaussian', mu=0.0, sigma=0.01, noiseAbsoluteOffset=0.0, noiseRelativeOffset=0.0,
simulateRain=False, rainfallRate=0.0,
addMesh=True,
exportLAS=False, exportHDF=False, exportCSV=False, exportSingleFrames=False,
exportRenderedImage=False, exportSegmentedImage=False, exportPascalVoc=False, exportDepthmap=False, depthMinDistance=0.0, depthMaxDistance=100.0,
dataFilePath="//output", dataFileName="output file",
debugLines=False, debugOutput=False, outputProgress=True, measureTime=False, singleRay=False, destinationObject=None, targetObject=None
)
# Velodyne
range_scanner.ui.user_interface.scan_rotating(
bpy.context,
scannerObject=bpy.context.scene.objects["Camera"],
xStepDegree=0.2, fovX=30.0, yStepDegree=0.33, fovY=40.0, rotationsPerSecond=20,
reflectivityLower=0.0, distanceLower=0.0, reflectivityUpper=0.0, distanceUpper=99999.9, maxReflectionDepth=10,
enableAnimation=False, frameStart=1, frameEnd=1, frameStep=1, frameRate=1,
addNoise=False, noiseType='gaussian', mu=0.0, sigma=0.01, noiseAbsoluteOffset=0.0, noiseRelativeOffset=0.0,
simulateRain=False, rainfallRate=0.0,
addMesh=True,
exportLAS=False, exportHDF=False, exportCSV=False, exportSingleFrames=False,
dataFilePath="//output", dataFileName="output file",
debugLines=False, debugOutput=False, outputProgress=True, measureTime=False, singleRay=False, destinationObject=None, targetObject=None
)
# Sonar
range_scanner.ui.user_interface.scan_sonar(
bpy.context,
scannerObject=bpy.context.scene.objects["Camera"],
maxDistance=100.0, fovSonar=135.0, sonarStepDegree=0.25, sonarMode3D=True, sonarKeepRotation=False,
sourceLevel=220.0, noiseLevel=63.0, directivityIndex=20.0, processingGain=10.0, receptionThreshold=10.0,
simulateWaterProfile=True, depthList= [
(15.0, 1.333, 1.0),
(14.0, 1.0, 1.1),
(12.5, 1.52, 1.3),
(11.23, 1.4, 1.1),
(7.5, 1.2, 1.4),
(5.0, 1.333, 1.5),
],
enableAnimation=True, frameStart=1, frameEnd=1, frameStep=1,
addNoise=False, noiseType='gaussian', mu=0.0, sigma=0.01, noiseAbsoluteOffset=0.0, noiseRelativeOffset=0.0,
simulateRain=False, rainfallRate=0.0,
addMesh=True,
exportLAS=False, exportHDF=False, exportCSV=False, exportSingleFrames=False,
dataFilePath="//output", dataFileName="output file",
debugLines=False, debugOutput=False, outputProgress=True, measureTime=False, singleRay=False, destinationObject=None, targetObject=None
) The script can then be run by executing blender myscene.blend --background --python myscript.py on the command line.
All generated data can be shown inside Blender by enabling the Add datapoint mesh option inside the Visualization submenu. It is also possible to visualize the data as rendered, segmented/labeled and depth images (see Export).
To render .las files the tool CloudCompare can be used.
You can further use Potree Desktop to visualize the raw data. The generated .las files can be converted automatically by dragging it into the window or manually by using the Potree Converter:
.\path\to\potree\PotreeConverter.exe .\path\to\data\data.las -o .\output_directory
This will generate a cloud.js file which you can drag and drop inside the Potree viewer.
See examples folder.
The .blend files contain preconfigured scenes. Example outputs are located inside the output folder, the used models can be found inside the models directory.
To generate a random landscape scene, run the following command on the command line:
python generate_landscapes.py
All parameters can be adjusted inside landscape.py. Example scenes are located inside the generated folder.
This add-on is developed using Visual Studio Code and the Blender extension blender_vscode.
To run the add-on in debug mode, use the extension and start the addon from there.
If you want to have autocomplete features, consider installing the fake-bpy-module package.
Feel free to fork, modify and improve our work! We would also appreciate to receive contributions in for of pull requests. For that it would be a good idea to open an issue with your idea.
This add-on was developed by Lorenzo Neumann at TU Bergakademie Freiberg.
Master thesis: Lorenzo Neumann. "Generation of 3D training data for AI applications by simulation of ranging methods in virtual environments", 2020.
Paper: Reitmann, S.; Neumann, L.; Jung, B. BLAINDER—A Blender AI Add-On for Generation of Semantically Labeled Depth-Sensing Data. Sensors 2021, 21, 2144. https://doi.org/10.3390/s21062144
Copyright (C) 2021 Lorenzo Neumann
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program. If not, see https://www.gnu.org/licenses/.
A brief summary of this license can be found here: https://tldrlegal.com/license/gnu-general-public-license-v3-(gpl-3)
Commercial license: If you want to use this software without complying with the conditions of the GPL-3.0 license, you can get a custom license for 5.000€ per application instance (= 1 VM or 1 computer). Discounts are possible when purchasing multiple licenses. If you wish to obtain such a license, please feel free to contact me at lorenzo.neumann@informatik.tu-freiberg.de or via an issue.
Chair model used: Low Poly Chair