Awesome

Gaussian Viewer

This repository contains a proof-of-concept web viewer for 3D Gaussian Splatting, a technique capable of rasterizing realistic scenes in real-time.

It combines the original paper diff-gaussian-rasterization implementation with hardware H264 encoding and WebRTC streaming to stream interactive views in realtime.

Live Demo

A live viewer is available at:

Hugging Face

Prerequisites

Python 3 or above
NVIDIA card with display driver 525.xx.xx or above
CUDA Toolkit 11.7

and least one of the following:

Ubuntu 22.04 (Linux)
WSL2 with Ubuntu 22.04 (Windows)
Docker

Cloning this Repository

To clone this repository and its dependencies, check it out with

git clone https://github.com/dylanebert/gaussian-viewer.git --recursive

Be sure to use the --recursive flag to clone the local dependencies:

Downloading Models

This project is currently hardcoded to run the trained bicycle example from the Mip-NeRF 360 dataset.

Click here to download the data. Unzip the archive, then copy the contained bicycle folder to this repository's models folder.

Running locally

This project depends on the VideoProcessingFramework for hardware tensor-to-video encoding and diff-gaussian-rasterization for rasterization. VideoProcessingFramework in particular has very specific requirements, including Ubuntu 22.04, or WSL2 with Ubuntu 22.04 on Windows. Alternatively, you can follow the Docker instructions to run in other environments.

Without Docker

Create a virtual python environment

python -m venv venv

Install the required python packages

python -m pip install -r requirements.txt
python -m pip install diff-gaussian-rasterization/
python -m pip install VideoProcessingFramework/
python -m pip install VideoProcessingFramework/src/PytorchNvCodec/

If you encounter issues, be sure the required submodules are present

git submodule update --init --recursive

and refer to corresponding submodule documentation for troubleshooting.

Run the server

python -m uvicorn main:app

This should print a local web address running the demo (i.e. http://localhost:8000)

With Docker

Edit the Dockerfile

The provided Dockerfile is configured to serve the viewer with an SSL certificate over HTTPS. This isn't necessary for running locally.

Replace the last two lines

EXPOSE 443

CMD ["python3.10", "-m", "uvicorn", "main:app", "--host", "0.0.0.0", "--port", "443", "--ssl-keyfile", "privkey.pem", "--ssl-certfile", "fullchain.pem"]

with

EXPOSE 8000

CMD ["python3.10", "-m", "uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

Build the container

docker build -t gaussian-viewer .

Run the container

docker run --gpus all -p 8000:8000 gaussian-viewer

Local Configuration

Change Addresses

When running locally, you will need to update the addresses to point toward the local implementation, rather than the remote server.

In gaussian-viewer-frontend/src/src/routes/+page.svelte, replace https://viewer.dylanebert.com/ with http://localhost:8000/.
In main.py, replace origins = ["https://viewer.dylanebert.com", "https://dylanebert-gaussian-viewer.hf.space"] with origins = ["*"] to allow all origins.

STUN/TURN

WebRTC typically requires STUN/TURN servers to function on web. However, this shouldn't be an issue when running locally.

The provided implementation is configured to fetch ICE servers from twilio using private credentials. You can provide your own credentials, replace them with other ICE servers, or ignore them when running locally.

To ignore them when running locally, in gaussian-viewer-frontend/src/src/routes/+page.svelte in connectToPeer, remove the iceServers fetch and simply pass iceServers: [].

Project Layout

This project consists of a python backend and svelte frontend.

Backend
- main.py
  - src/
    - camera.py
    - gaussian_model.py
    - render.py
    - turn.py
    - utils.py
Frontend (gaussian-viewer-frontend/)
src/
- src/routes/+page.svelte
public/

The backend server entrypoint is main.py.

All functional frontend code is in +page.svelte. This is built to static HTML that is served in public/.

Community

If you're interested in community discussion on this project, join the Hugging Face discord and feel free to ping me @IndividualKex. Alternatively, email me at individualkex@gmail.com.