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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Easy NeRF synthetic dataset creation within Blender
| Date | Stars |
|---|---|
| 2026-07-24 | 1009 |
| 2026-07-25 | 1009 |
| 2026-07-28 | 1009 |
| 2026-07-30 | 1009 |
| 2026-08-06 | 1009 |
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# BlenderNeRF Whether a VFX artist, a research fellow or a graphics amateur, **BlenderNeRF** is the easiest and fastest way to create synthetic NeRF and Gaussian Splatting datasets within Blender. Obtain renders and camera parameters with a single click, while having full user control over the 3D scene and camera! <p align='center'> <a href="https://youtu.be/C8YuDoU11cg"><img src="https://img.youtube.com/vi/C8YuDoU11cg/maxresdefault.jpg" width='90%'></a> <br> Are you ready to NeRF? Start with a single click in Blender by checking out <a href="https://youtu.be/C8YuDoU11cg">this tutorial</a>! </p> ## Neural Radiance Fields **Neural Radiance Fields ([NeRF](https://www.matthewtancik.com/nerf))** aim at representing a 3D scene as a view dependent volumetric object from 2D images only, alongside their respective camera information. The 3D scene is reverse engineered from the training images with help of a simple neural network. [**Gaussian Splatting**](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/) is a follow-up method for rendering radiance fields in a point-based manner. This representation is highly optimised for GPU rendering and leverages more traditional graphics techniques to achieve high frame rates. I recommend watching [this YouTube video](https://www.youtube.com/watch?v=YX5AoaWrowY) by **Corridor Crew** for a thrilling investigation on a few use cases and future potential applications of NeRFs. ## Motivation Rendering is an expensive computation. Photorealistic scenes can take seconds to hours to render depending on the scene complexity, hardware and available software resources. NeRFs and Gaussian splats can speed up this process, but require camera information typically extracted via cumbersome code. This plugin enables anyone to get renders and cameras with a single click in Blender. <p align='center'> <img src='https://maximeraafat.github.io/assets/posts/blendernerf/BlenderNeRF_compressed.gif' width='90%'/> </p> ## Installation 1. Download this repository as a **ZIP** file 2. Open Blender (4.0.0 or above) 3. In Blender, head to **Edit > Preferences > Add-ons**, and select **Install From Disk** under the drop icon 4. Select the downloaded **ZIP** file Although release versions of **BlenderNeRF** are available for download, they are primarily intended for tracking major code changes and for citation purposes. I recommend downloading the current repository directly, since minor changes or bug fixes might not be included in a release right away. ## Setting **BlenderNeRF** consists of 3 methods discussed in the sub-sections below. Each method is capable of creating **training** data and **testing** data for NeRF in the form of training images and a `transforms_train.json` respectively `transforms_test.json` file with the corresponding camera information. The data is archived into a single **ZIP** file containing training and testing folders. Training data can then be used by a NeRF model to learn the 3D scene representation. Once trained, the model may be evaluated (or tested) on the testing data (camera information only) to obtain novel renders. ### Subset of Frames **Subset of Frames (SOF)** renders every **N** frames from a camera animation, and utilises the rendered subset of frames as NeRF training data. The registered testing data spans over all frames of the same camera animation, including training frames. When trained, the NeRF model can render the full camera animation and is consequently well suited for interpolating or rendering large animations of static scenes. <p align='center'> <img src='https://maximeraafat.github.io/assets/posts/blendernerf/SOF.gif' width='90%'/> </p> ### Train and Test Cameras **Train and Test Cameras (TTC)** registers training and testing data from two separate user defined cameras. A NeRF model can then be fitted with the data extracted from the training camera, and be evaluated on the testing data. <p align='center'> <img src='https://
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Nicholas Sharp · United States
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:01bb5ec29ca9c0fa, topic:computer-vision, topic:nerf, topic:gaussian-splatting