OpenSplat

OpenSplat

** OpenSplat has joined the WebODM ecosystem!**

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 ODX, OpenSfM, COLMAP, OpenMVG or nerfstudio project format and computes a scene file (.ply, .splat, .spz, or .rad) that can be later imported for viewing, editing and rendering in other software.

Graphics card recommended, but not required! OpenSplat runs the fastest on NVIDIA, AMD and Apple (Metal) GPUs, but can also run entirely on the CPU (~100x slower).

Getting Started

If you're on Windows, you can buy the pre-built program. This saves you time and helps support the project ❤️. Then jump directly to the run section. As an alternative, check the build section below.

If you're on macOS or Linux check the build section below.

Build

You can build OpenSplat with or without GPU support.

Requirements for all builds:

• OpenCV: sudo apt install libopencv-dev should do it.
• libtorch: See instructions below.

CPU

For libtorch visit https://pytorch.org/get-started/locally/ and select your OS, for package select "LibTorch". For compute platform you can select "CPU".

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)

CUDA

Additional requirement:

• 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

For 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.

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)

ROCm via HIP

Additional requirement:

• 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

For 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.

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

Windows

There's several ways to build on Windows, but this particular configuration has been confirmed to work:

• Visual Studio 2022 C++
• https://github.com/Kitware/CMake/releases/download/v3.28.3/cmake-3.28.3-windows-x86_64.msi
• https://developer.download.nvidia.com/compute/cuda/11.8.0/network_installers/cuda_11.8.0_windows_network.exe
• https://download.pytorch.org/libtorch/cu118/libtorch-win-shared-with-deps-2.1.2%2Bcu118.zip
• https://github.com/opencv/opencv/releases/download/4.9.0/opencv-4.9.0-windows.exe

Then run:

"C:/Program Files/Microsoft Visual Studio/2022/Community/VC/Auxiliary/Build/vcvars64.bat"
git clone https://github.com/pierotofy/OpenSplat OpenSplat
cd OpenSplat
md build
cd build
cmake -DCMAKE_PREFIX_PATH=C:/path_to/libtorch_2.1.2_cu11.8/libtorch -DOPENCV_DIR=C:/path_to/OpenCV_4.9.0/build -DCMAKE_BUILD_TYPE=Release ..
cmake --build . --config Release

Optional: Edit cuda target (only if required) before cmake --build .

C:/path_to/OpenSplat/build/gsplat.vcxproj for example: arch=compute_75,code=sm_75

macOS

If you're using Homebrew, you can install Cmake/OpenCV/Pytorch by running:

brew install cmake
brew install opencv
brew install pytorch

You will also need to install Xcode and the Xcode command line tools to compile with metal support (otherwise, OpenSplat will build with CPU acceleration only):

1. Install Xcode from the Apple App Store.
2. Install the command line tools with xcode-select --install. This might do nothing on your machine.
3. If xcode-select --print-path prints /Library/Developer/CommandLineTools,then run sudo xcode-select --switch /Applications/Xcode.app/Contents/Developer.
4. On recent Xcode versions the Metal toolchain is a separate download. If xcrun -sdk macosx metal --version fails, run xcodebuild -downloadComponent MetalToolchain.

Then run:

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

On macOS GPU_RUNTIME defaults to MPS (metal acceleration). If the Metal compiler isn't available, the build automatically falls back to CPU. To force a CPU-only build, pass -DGPU_RUNTIME=CPU.

:warning: You will probably get a libc10.dylib can’t be opened because Apple cannot check it for malicious software error on first run. Open System Settings and go to Privacy & Security and find the Allow button. You might need to repeat this several times until all torch libraries are loaded.

:warning: If you get a Library not loaded: @rpath/libomp.dylib error, try running brew link libomp --force before running OpenSplat.

Docker Build

CUDA

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 CMAKE_CUDA_ARCHITECTURES="70;75;80" \
  --build-arg CMAKE_BUILD_TYPE=Release .

ROCm via HIP

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 .

Note: If you want to use ROCm 6.x, you need to switch to AMD version of pytorch docker as a base layer to build:

docker build \
  -t opensplat:ubuntu-22.04-libtorch-2.1.2-rocm-6.0.2 \
  -f Dockerfile.rocm6 .

Run

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

Then run from a command line prompt:

Windows

cd c:\path\to\opensplat
opensplat.exe c:\path\to\banana -n 2000

macOS / Linux

cd build
./opensplat /path/to/banana -n 2000

The program will generate an output splat.ply file which 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. The program can also output a cameras JSON via --output-cameras cameras.json.

To run on your own data, choose the path to an existing COLMAP, OpenSfM, ODX or nerfstudio project. The project must have sparse points included (random initialization is not supported, see https://github.com/pierotofy/OpenSplat/issues/7).

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

./opensplat --help

Google Colab

To run OpenSplat in Google Colab follow this example notebook.

Compression

To generate compressed splats (.splat files), use the -o option:

./opensplat /path/to/banana -o banana.splat

Resume

You can resume training of a .PLY file by using the --resume option:

./opensplat /path/to/banana --resume /path/to/splat.ply

Image Masks

You can exclude parts of your images by adding 2D masks. Place them in a masks folder (also recognized: mask, segmentation, dynamic_masks) inside your project, named after each image (e.g. images/IMG_001.JPG → masks/IMG_001.png). Masks are grayscale images matching the input dimensions: white marks pixels to keep, black pixels to ignore.

When masks are found they are applied automatically. Use --no-masks to ignore them.

Coordinate Reference System

By default OpenSplat preserves the input coordinate reference system of the model. If you want to automatically center the result so that it displays nicely in most viewers, use --center.

AMD GPU Notes

To train a model with AMD GPU using docker container, you can use the following command as a reference:

1. Launch the docker container with the following command:

docker run -it -v ~/data:/data --device=/dev/kfd --device=/dev/dri opensplat:ubuntu-22.04-libtorch-2.1.2-rocm-6.0.2 bash

1. Inside the docker container, run the following command to train the model:

export HIP_VISIBLE_DEVICES=0
export HSA_OVERRIDE_GFX_VERSION=10.3.0  # AMD RX 6700 XT workaround 
cd /code/build
./opensplat /data/banana -n 2000

Project Goals

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

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

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

Contributing

We welcome contributions! Pull requests are welcome.

GPU Memory Notes

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

Credits

The methods used in OpenSplat are originally based on [splatfacto](https://docs.nerf.st