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Runs on Windows, Mac and Linux License AGPL-3.0 license 319 stars 23 forks Branches Tags Activity Star Notifications * Code * Issues 18 * Pull requests 0 * Discussions * Actions * Projects 0 * Security * Insights Additional navigation options * Code * Issues * Pull requests * Discussions * Actions * Projects * Security * Insights pierotofy/OpenSplat This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. main BranchesTags Go to file Code Folders and files Last commit Last Name Name message commit date Latest commit History 192 Commits .github/workflows .github/workflows vendor vendor CMakeLists.txt CMakeLists.txt CODE_OF_CONDUCT.md CODE_OF_CONDUCT.md Dockerfile Dockerfile Dockerfile.rocm Dockerfile.rocm Dockerfile.rocm6 Dockerfile.rocm6 LICENSE.txt LICENSE.txt README.md README.md colmap.cpp colmap.cpp colmap.hpp colmap.hpp constants.hpp constants.hpp cv_utils.cpp cv_utils.cpp cv_utils.hpp cv_utils.hpp gsplat.hpp gsplat.hpp input_data.cpp input_data.cpp input_data.hpp input_data.hpp kdtree_tensor.cpp kdtree_tensor.cpp kdtree_tensor.hpp kdtree_tensor.hpp model.cpp model.cpp model.hpp model.hpp nerfstudio.cpp nerfstudio.cpp nerfstudio.hpp nerfstudio.hpp opensplat.cpp opensplat.cpp opensplat.hpp opensplat.hpp optim_scheduler.cpp optim_scheduler.cpp optim_scheduler.hpp optim_scheduler.hpp point_io.cpp point_io.cpp point_io.hpp point_io.hpp project_gaussians.cpp project_gaussians.cpp project_gaussians.hpp project_gaussians.hpp rasterize_gaussians.cpp rasterize_gaussians.cpp rasterize_gaussians.hpp rasterize_gaussians.hpp simple_trainer.cpp simple_trainer.cpp spherical_harmonics.cpp spherical_harmonics.cpp spherical_harmonics.hpp spherical_harmonics.hpp ssim.cpp ssim.cpp ssim.hpp ssim.hpp tensor_math.cpp tensor_math.cpp tensor_math.hpp tensor_math.hpp tile_bounds.hpp tile_bounds.hpp utils.cpp utils.cpp utils.hpp utils.hpp View all files Repository files navigation * README * Code of conduct * AGPL-3.0 license OpenSplat A free and open source implementation of 3D gaussian splatting written in C++, focused on being portable, lean and fast. [314984262-c9327c7c-31ad-402d-a5a] [314667230-eba4ae75-2c88-4c9e-a66] 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. Graphics card recommended, but not required! OpenSplat runs the fastest on NVIDIA and AMD GPUs, but can also run entirely on the CPU (~100x slower). Commercial use allowed and encouraged under the terms of the AGPLv3. 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 mkdir build && cd build cmake -DCMAKE_PREFIX_PATH=C:/path_to/libtorch_2.1.2_cu11.8 -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 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$(nproc) ./opensplat [?][?] 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. 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 TORCH_CUDA_ARCH_LIST="7.0;7.5" \ --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 ] [ 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 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 2. 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 * Add Metal support on macOS * Distributed computation using multiple machines * Real-time training viewer output * Compressed scene outputs * 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. License 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 (c) 2023 The Nerfstudio Team. About Free and open source 3D gaussian splatting in C++ with CPU and GPU (NVIDIA/AMD) support. Runs on Windows, Mac and Linux Topics gaussian 3d splatting splats radiance-field Resources Readme License AGPL-3.0 license Code of conduct Code of conduct Activity Stars 319 stars Watchers 12 watching Forks 23 forks Report repository Releases 5 1.1.0 Latest Mar 21, 2024 + 4 releases Packages 0 No packages published Contributors 6 * @pierotofy * @pfxuan * @salovision * @Disa-Kizonda * @lanxinger * @ichsan2895 Languages * C++ 94.4% * CMake 4.4% * Dockerfile 1.2% Footer (c) 2024 GitHub, Inc. Footer navigation * Terms * Privacy * Security * Status * Docs * Contact * Manage cookies * Do not share my personal information You can't perform that action at this time.