Haruhi-Suzumiya-3D-School
BlenderProc
| Haruhi-Suzumiya-3D-School | BlenderProc | |
|---|---|---|
| 4 | 15 | |
| 15 | 2,568 | |
| - | 2.8% | |
| 2.4 | 8.3 | |
| 8 months ago | 5 days ago | |
| Python | Python | |
| Creative Commons Zero v1.0 Universal | GNU General Public License v3.0 only |
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Haruhi-Suzumiya-3D-School
- Recreating the real life school from the anime "The Melancholy of Haruhi Suzumiya" in a 1:1 scale, open source
- Recreation of Haruhi Suzumiyas school in 3D 1:1 open source now!
- Made the Haruhi Suzumiya School in Blender, free on github
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Now opensource development, (available for download), thanks for the help 5 months ago.
Github Download: https://github.com/SquirrelModeller/Haruhi-Suzumiya-3D-School/releases/tag/0.4
BlenderProc
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Synthetic image Generation
Blender with add-ons (Kubric, BlenderProc)
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dataset collection for transfer learning
If you are interested, there are open source solutions on top of Unity, Blender, Unreal. You can generate yourself the data you described easier than it looks (the amount of options and settings can be intimidating with these tools).
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Are there any tools to generate images and labels from 3d models/games?
Blender addons like https://github.com/google-research/kubric and https://github.com/DLR-RM/BlenderProc
- How to get started with synthetic data generation?
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Course/Learning Material recommendations for getting started with Synthetic Data Generation for Computer Vision Models
I have been going through some papers and reviewing existing methods and I've come across stuff like UnrealCV (https://unrealcv.org/) and blenderproc (https://github.com/DLR-RM/BlenderProc).
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Searching for MIT CSAIL's IKEA dataset
I'm trying to use BlenderProc to automatically generate training data for object recognition.
- [P] BlenderProc2: Photorealistic Rendering of Procedurally Generated Scenes
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[D] What's the best method to generate synthetic data for an image with text? Small dataset
Check this out https://github.com/DLR-RM/BlenderProc. I haven't used it extensively, but it seems to decent for generating synthetic image data.
- Apple’s Machine Learning Team Introduces ‘Hypersim’: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding
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Using Blender for Computer Vision
That looks really interesting, have you seen BlenderProc: https://github.com/DLR-RM/BlenderProc it looks really similar just that BlenderProc already supports a vast variety of datasets and is fully documented.
What are some alternatives?
BlendLuxCore - Blender Integration for LuxCore
zpy - Synthetic data for computer vision. An open source toolkit using Blender and Python.
pyrender - Easy-to-use glTF 2.0-compliant OpenGL renderer for visualization of 3D scenes.
albumentations - Fast image augmentation library and an easy-to-use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
vedo - A python module for scientific analysis of 3D data based on VTK and Numpy
com.unity.perception - Perception toolkit for sim2real training and validation in Unity
pyntcloud - pyntcloud is a Python library for working with 3D point clouds.
segmentation_models.pytorch - Segmentation models with pretrained backbones. PyTorch.
nerfstudio - A collaboration friendly studio for NeRFs
agi2nerf - Simple tool for converting Agisoft XML files to NERF JSON files for https://github.com/NVlabs/instant-ngp
segmentation_models - Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
SingleViewReconstruction - Official Code: 3D Scene Reconstruction from a Single Viewport