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!

WARNING: a fully free and open source updated version with exhaustive documentation is available on GitHub under this link. Here you can however offer your support by treating me to a coffee :)

Neural Radiance Fields

Neural Radiance Fields (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 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 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.

How to NeRF (not included with the plugin)

If you have access to an NVIDIA GPU, you might want to install Instant NGP on your own device for an optimal user experience, by following the instructions provided on their repository. Otherwise, you can run NeRF in a COLAB notebook on Google GPUs for free with a Google account.

Open this COLAB notebook (also downloadable here) and follow the instructions.

Remarks

This add-on is being developed as a fun side project over the course of multiple months and versions of Blender, mainly on macOS. If you encounter any issues with the plugin functionalities, feel free to open a GitHub issue with a clear description of the problem, which BlenderNeRF version the issues have been experienced with, and any further information if relevant.

Real World Data

While this extension is intended for synthetic datasets creation, existing tools for importing motion tracking data from real world cameras are available. One such example is Tracky by Shopify, an open source iOS app and an adjacent Blender plugin recording motion tracking data from an ARKit session on iPhone. Keep in mind however that tracking data can be subject to drifts and inaccuracies, which might affect the resulting NeRF reconstruction quality.

Citation

If you find this repository useful in your research, please consider citing BlenderNeRF using the dedicated GitHub button above. If you made use of this extension for your artistic projects, feel free to share some of your work using the #blendernerf hashtag on social media! :)