https://github.com/risteon/blender_kitti

Render large point clouds and voxel grids with blender.
https://github.com/risteon/blender_kitti

blender cycles-renderer kitti-dataset-visualization point-cloud voxelization

Last synced: about 1 month ago
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Render large point clouds and voxel grids with blender.

• Host: GitHub
• URL: https://github.com/risteon/blender_kitti
• Owner: risteon
• License: mit
• Created: 2020-01-12T19:35:28.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2023-11-30T07:23:01.000Z (about 1 year ago)
• Last Synced: 2024-08-03T09:01:40.901Z (5 months ago)
• Topics: blender, cycles-renderer, kitti-dataset-visualization, point-cloud, voxelization
• Language: Python
• Homepage:
• Size: 20.8 MB
• Stars: 72
• Watchers: 3
• Forks: 8
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# blender-kitti

[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)

| ![KITTI Point Cloud](img/blender_kitti_render_point_cloud_main.png?raw=true "Main view") |![KITTI Point Cloud](img/blender_kitti_render_point_cloud_top.png?raw=true "Top view") |

|:-------------------------:|:-------------------------:|

| ![KITTI Point Cloud](img/blender_kitti_render_voxels_main.png?raw=true "Main view voxels") |![KITTI Point Cloud](img/blender_kitti_render_voxels_top.png?raw=true "Top view voxels") |

| ![KITTI Scene Flow](img/blender_kitti_render_scene_flow_main.png?raw=true "Main view scene flow") |![KITTI Scene Flow](img/blender_kitti_render_scene_flow_top.png?raw=true "Top view scene flow") |

## About

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.

## Installation into Blender's bundled python

```

# Wherever your Blender installation is located. E.g. cd /opt/blender-2.90.1-linux64/2.90/python

$ cd //python

# make pip available

$ ./bin/python3.Xm lib/python3.X/ensurepip

# install

$ ./bin/pip3 install -e 

```

## Render demo images

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()

```

## Work on a scene in Blender

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:

![KITTI Point Cloud](img/demo_point_cloud_random.png?raw=true "Main view")

## Ideas for future development

* 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)