NeRF

NeRF

Demonstrates a Keras NeRF, a neural radiance field model that learns a 3D scene from pictures, in a Streamlit page. For computer vision learners and 3D graphics hobbyists.

Made by keras-io on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face.

About

What it is: NeRF is a Streamlit app from the Keras team's keras-io group. By its name it demonstrates a neural radiance field, a way for a network to learn a 3D scene from pictures. The repository has a nerf folder with the model code, besides the app file and a package list.

Who it suits: It suits computer vision learners and 3D graphics hobbyists who want to see a NeRF in action, and developers who want a compact Keras example to read. The record gives no usage notes or output details, so the page is the guide.

Industry | Who uses it | How they use it | Example
Education | Computer vision students | See what a neural radiance field produces | Open the Space and follow the controls on the page
Graphics | Graphics hobbyists | Learn how a network can store a scene | Look at the views the app shows
Software | Python developers | Read Keras code for a NeRF | Browse the nerf folder next to app.py

You can skip it if you need to build a 3D model from your own photos, since the record does not say the app takes uploads.

Where to use it:

• A web browser, on the Space's page on Hugging Face
• Windows, macOS or Linux with Python, from a downloaded copy

Use case | Who | What they get back
Try a NeRF demo | Learners | Views produced by a trained model
Study the code | Developers | A nerf folder with Keras model code

How to set it up:

• Nothing at all if you use the online Space
• Python on your computer, only if you run a copy
• The packages listed in requirements.txt

1. Open the Space's page in a browser to use it with nothing to install.
2. To run a copy, install the Hugging Face Hub package and download the Space.
3. Move into the new folder, then install Streamlit and the listed requirements.
4. Start the app with Streamlit.

pip install -U huggingface_hub
hf download keras-io/NeRF --repo-type space --local-dir NeRF
cd NeRF
pip install streamlit
pip install -r requirements.txt
streamlit run app.py

How to use it: Online, open the Space and use the controls on its page. On your own machine, run the main file with Streamlit from the Space folder, then use the page it serves. The record does not list the controls, so look at the page.

streamlit run app.py

Example: You open the Space and use its controls to view what the NeRF model produces for a scene. The record shows no sample, so the controls and the scene depend on the page.

For newcomers and experts: A NeRF learns how light and color look from many viewpoints of one scene, so it can draw views it never saw. Start by opening the Space and trying the controls to see what appears.

If you know the field, a NeRF is a small network mapping a 3D position and view direction to color and density, which volume rendering turns into an image. The record gives no training data, settings or quality figures.

You set a viewpoint on the page
The app queries the NeRF network along camera rays
Volume rendering combines the results
The page shows the rendered view

Good to know:

• License: afl-3, as listed on the Space
• Cost to run: none named
• Data it reaches: only what the page takes as input, and the code in the repository
• Not for: the record names no exclusions, and it gives no quality or speed figures

On Hugging Face, 13 November 2026

• Likes: 25
• Built with: streamlit
• License: afl-3.0

Links

• Space: https://huggingface.co/spaces/keras-io/NeRF
• Files: https://huggingface.co/spaces/keras-io/NeRF/tree/main

Try it

Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself.