This repository contains some code to create large particle collections (voxel grids, point clouds) together with color information in Blender. blender-kitti has two goals in mind:

• The created objects are exact, meaning that all particles are created at their defined location and all colors have the exact RGB-value as specified. All particles can be colored individually.
• Performance of the scrips is acceptable. It should not take much longer than a second to create a 100k point cloud.

Together, these qualities enable blender-kitti to render large scale data from the KITTI dataset (hence the name) or related datasets.

When it comes to visualization, everyone has a different usecase. So this is not a one-fits-all solution but rather a collection of techniques that can be adapted to individual usecases.

There is example code that renders the demo images above. Use this to verify that your installation works and as a starting point for your modifications.

# Wherever your Blender installation is located. E.g. cd /opt/blender-2.90.1-linux64/2.90/python
$ cd <blender_directory>/<blender_version>/python

# make pip available
$ ./bin/python3.Xm lib/python3.X/ensurepip

# install
$ ./bin/pip3 install -e <path_to_blender_kitti>

Render the bundled KITTI point cloud with semantic coloring from two different camera perspectives. This writes two image files to the /tmp folder.

$ blender --background --python-console

>>> import blender_kitti_examples
>>> blender_kitti_examples.render_kitti_point_cloud()

Render the bundled Semantic KITTI voxel grid as top view and close-up image. This writes two image files to the /tmp folder.

$ blender --background --python-console

>>> import blender_kitti_examples
>>> blender_kitti_examples.render_kitti_voxels()

Render the bundled KITTI point cloud with pseudo odometry for hsv colored scene flow from two different camera perspectives. This writes two image files to the /tmp folder.

$ blender --background --python-console

>>> import blender_kitti_examples
>>> blender_kitti_examples.render_kitti_scene_flow()

You can import and use blender-kitti in the python console window in the Blender-GUI itself to work on a given scene.

# Create a random [Nx3] numpy array and add as point cloud to a scene in blender.
import bpy
import numpy as np
from blender_kitti import add_point_cloud

# create some points
points = np.random.normal(loc=0.0, scale=2.0, size=(100, 3))

# get current scene
scene = bpy.context.scene

# create point cloud object and link to scene
add_point_cloud(points=points, scene=scene, particle_radius=0.2)

Result:

• Track all created objects/meshes/images and be able to completely remove them later
• Handle name clashes or overwrite existing objects
• Define the rotation/scale of individual particles
• Create a useful, small API
• Finally be able to build Blender-bpy as module (Dockerfile)