A free and open source implementation of 3D gaussian splatting written in C++, focused on being portable, lean and fast.

OpenSplat takes camera poses + sparse points in COLMAP or nerfstudio project format and computes a scene file (.ply) that can be later imported for viewing, editing and rendering in other software.

Commercial use allowed and encouraged under the terms of the AGPLv3. ✅

Requirements:

• CUDA: Make sure you have the CUDA compiler (nvcc) in your PATH and that nvidia-smi is working. https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html
• libtorch: Visit https://pytorch.org/get-started/locally/ and select your OS, for package select "LibTorch". Make sure to match your version of CUDA if you want to leverage GPU support in libtorch.
• OpenCV: sudo apt install libopencv-dev should do it.

Then:

git clone https://github.com/pierotofy/OpenSplat OpenSplat
cd OpenSplat
mkdir build && cd build
cmake -DCMAKE_PREFIX_PATH=/path/to/libtorch/ .. && make -j$(nproc)

The software has been tested on Ubuntu 20.04 and Windows. With some changes it could run on macOS (help us by opening a PR?).

Requirements:

• ROCm: Make sure you have the ROCm installed at /opt/rocm. https://rocm.docs.amd.com/projects/install-on-linux/en/latest/tutorial/quick-start.html
• libtorch: Visit https://pytorch.org/get-started/locally/ and select your OS, for package select "LibTorch". Make sure to match your version of ROCm (5.7) if you want to leverage AMD GPU support in libtorch.
• OpenCV: sudo apt install libopencv-dev should do it.

Then:

git clone https://github.com/pierotofy/OpenSplat OpenSplat
cd OpenSplat
mkdir build && cd build
export PYTORCH_ROCM_ARCH=gfx906
cmake -DCMAKE_PREFIX_PATH=/path/to/libtorch/ -DGPU_RUNTIME="HIP" -DHIP_ROOT_DIR=/opt/rocm -DOPENSPLAT_BUILD_SIMPLE_TRAINER=ON ..
make

In addition, you can leverage Jinja to build the project

cmake -GNinja -DCMAKE_PREFIX_PATH=/path/to/libtorch/ -DGPU_RUNTIME="HIP" -DHIP_ROOT_DIR=/opt/rocm -DOPENSPLAT_BUILD_SIMPLE_TRAINER=ON ..
jinja

Navigate to the root directory of OpenSplat repo that has Dockerfile and run the following command to build the Docker image:

docker build -t opensplat .

The -t flag and other --build-arg let you tag and further customize your image across different ubuntu versions, CUDA/libtorch stacks, and hardware accelerators. For example, to build an image with Ubuntu 22.04, CUDA 12.1.1, libtorch 2.2.1, and support for CUDA architectures 7.0 and 7.5, run the following command:

docker build \
  -t opensplat:ubuntu-22.04-cuda-12.1.1-torch-2.2.1 \
  --build-arg UBUNTU_VERSION=22.04 \
  --build-arg CUDA_VERSION=12.1.1 \
  --build-arg TORCH_VERSION=2.2.1 \
  --build-arg TORCH_CUDA_ARCH_LIST="7.0;7.5" \
  --build-arg CMAKE_BUILD_TYPE=Release .

Navigate to the root directory of OpenSplat repo that has Dockerfile and run the following command to build the Docker image:

docker build -t opensplat -f Dockerfile.rocm .

The -t flag and other --build-arg let you tag and further customize your image across different ubuntu versions, CUDA/libtorch stacks, and hardware accelerators. For example, to build an image with Ubuntu 22.04, CUDA 12.1.1, libtorch 2.2.1, ROCm 5.7.1, and support for ROCm architectures gfx906, run the following command:

docker build \
  -t opensplat:ubuntu-22.04-cuda-12.1.1-libtorch-2.2.1-rocm-5.7.1-llvm-16 \
  --build-arg UBUNTU_VERSION=22.04 \
  --build-arg CUDA_VERSION=12.1.1 \
  --build-arg TORCH_VERSION=2.2.1 \
  --build-arg ROCM_VERSION=5.7.1 \
  --build-arg PYTORCH_ROCM_ARCH="gfx906" \
  --build-arg CMAKE_BUILD_TYPE=Release .

To get started, download a dataset and extract it to a folder: [ banana ] [ train ] [ truck ]

Then run:

./opensplat /path/to/banana -n 2000
[...]
Wrote splat.ply

The output splat.ply can then be dragged and dropped in one of the many viewers such as https://playcanvas.com/viewer. You can also edit/cleanup the scene using https://playcanvas.com/supersplat/editor

To run on your own data, choose the path to an existing COLMAP or nerfstudio project. The project must have sparse points included (random initialization is not supported, see #7).

There's several parameters you can tune. To view the full list:

./opensplat --help

We recently released OpenSplat, so there's lots of work to do.

• Support for running on AMD cards (more testing needed)
• Support for running on CPU-only
• Improve speed / reduce memory usage
• Distributed computation using multiple machines
• Real-time training viewer output
• Compressed scene outputs
• Your ideas?

https://github.com/pierotofy/OpenSplat/issues?q=is%3Aopen+is%3Aissue+label%3Aenhancement

We welcome contributions! Pull requests are welcome.

A single gaussian takes ~2000 bytes of memory, so currenly you need ~2GB of GPU memory for each million gaussians.

The methods used in OpenSplat are originally based on splatfacto.

The code in this repository, unless otherwise noted, is licensed under the AGPLv3.

The code from splatfacto is originally licensed under the Apache 2.0 license and is © 2023 The Nerfstudio Team.