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minGS

This is a minimalistic refactor of the original 3D Gaussian splatting codebase that follows PyTorch conventions and allow for easy customization and extension, based on the original 3DGS official repository.

It is meant for researchers who want to experiment with 3D Gaussian splatting and need a clean and easy to understand codebase to start from.

Features

  • 🧑🏻‍💻 Typed and commented
  • 📦 .devcontainer Docker provided
  • 📄 Separation of model and training logic
    • A nn.module GaussianModel only for storing parameters and forward pass (rendering)
    • Reference training logic and hyperparameters is defined in train() in gs.trainers.basic
  • 📸 Viser web-based frontend for viewing model during and after training

Examples

Minimal Custom Training Example

To customize the pipeline GaussianModel can be used just like any other PyTorch model and the training loop can be written from scratch. Below is a minimal example:

import torch
from gs.core.GaussianModel import GaussianModel
from gs.helpers.loss import l1_loss
from gs.io.colmap import load

cameras, pointcloud = load('your_dataset/')
model = GaussianModel.from_point_cloud(pointcloud).cuda()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001, eps=1e-15)

for i in range(5000):

    camera = cameras[i % len(cameras)]
    rendered = model.forward(camera)

    loss = l1_loss(rendered, camera.image)
    loss.backward()

    optimizer.step()
    optimizer.zero_grad(set_to_none=True) 

    torch.cuda.empty_cache()

Structure

The codebase is structured as follows:

  • gs/: The Gaussian splatting module.
    • core/: Core data structures and functions for rendering 3DGS models
      • BaseCamera.py: Base class that represents a camera used for training 3DGS models
      • BasePointCloud.py: Base class for point clouds used for initializing 3DGS models
      • GaussianModel.py: 3DGS model refactored as a nn.Module. Use forward with a camera to render the model
    • io/: Functions for importing and exporting image and point cloud data
      • colmap/: Functions for importing COLMAP reconstructions into BaseCamera and BasePointCloud compliant objects
    • trainers/: Training scripts for 3DGS models
      • basic/: Re-implementations of the original training script
    • visualization/: Classes for visualizing 3DGS models
      • Viewer.py: Class for starting a web-based 3DGS viewer for a GaussianModel
      • TrainingViewer.py Extension of Viewer to be integrated into a training loop for live viewing during training
    • helpers/: General functions for rendering and training 3DGS models

Installing dependencies

Only tested for Linux, but may work for Windows too. Using devcontainers should make getting the dependencies easier.

Method 1: Local environment (Tested on Python 3.8)

  • CUDA Toolkit
  • pip install numpy scipy torch torchvision torchaudio plyfile lpips pybind11 viser
  • Installing PyBind11 submodules
    • pip install -e ./submodules/diff-gaussian-rasterization/
    • pip install -e ./submodules/simple-knn/

Method 2: .devcontainer

Devcontainers automatically recreate the development environment using Docker. It is mainly supported by VSCode but there is also limited support for other editors. Install the relevant extensions, and when you open the repository you should be prompted to enter the container environment. First time running might take around 5 minutes to build the environment.