# spirula-studio
**Repository Path**: jxx315_admin/spirula-studio
## Basic Information
- **Project Name**: spirula-studio
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: GPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-09-20
- **Last Updated**: 2026-09-20
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# Spirula Studio
[**Download**](https://github.com/harry7557558/spirula-studio/releases/) •
[**Build from Source**](#build-from-source) •
[**Gallery**](#gallery) •
[**Web Viewer**](https://harry7557558.github.io/spirula-studio/viewer/)
Spirula Studio trains 3D Gaussian Splatting models – from raw photo/video to splat to textured mesh – in one self-contained binary. No Python/PyTorch, no separate COLMAP install. Runs on NVIDIA, AMD, Intel, and Apple GPUs via Vulkan, trains 10M full-SH Gaussians in 8 GB VRAM, and has native support for fisheye and 360° cameras.
Dataset credit: [Garage](https://www.kaggle.com/datasets/simonbethke/garage) by Simon Bethke (CC BY-SA 4.0); Flight Systems and Control Lab at UTIAS; MegaDepth-X; Mip-NeRF 360.
## Features
- Cross vendor support via Vulkan compute – Runs on **NVIDIA, AMD, Intel, and Apple** GPUs
- One strategy combining advantages of **MCMC/IGS+/MRNF** – Sharper results, fewer floaters, from objects to large scenes
- Extreme **VRAM efficiency** with quantized training – Up to 10 million SH3 Gaussians in 8GB VRAM
- Native **360° camera** and **equirectangular** support – Load a dataset and train, no undistortion needed
- Modified **Bilateral grid** and **PPISP** for exposure/WB correction – Improving quality without unwanted color shift or darkening
- Built-in **lightning-fast SfM**, **AI masking**, frame extraction from videos – No need to wait for COLMAP or run separate scripts
- Depth/normal, meshing, skybox, linear color... And more.
## News
- **September 10, 2026: Metric scale** – The dataset creation module now uses telemetry metadata in common video and image formats to recover metric scale and orientation, addressing the popular report that reconstruction results are too large/small or tilted.
- **September 3, 2026: LoMa feature support** – The SfM module now supports [LoMa](https://github.com/davnords/LoMa) for feature detection and matching on difficult datasets.
- **August 14, 2026: macOS support** – Support for training on macOS/Apple Silicon has been validated. The app can now be downloaded from [Releases page](https://github.com/harry7557558/spirula-studio/releases/).
- **August 8, 2026: Multilingual support** – Multilingual support has been added, available to both GUI and CLI. Supported languages: English, 日本語, 简体中文, 繁體中文, 한국어, Deutsch, Français, Español, Português, Italiano, Nederlands, Русский, Türkçe.
- **August 8, 2026: End-to-end workflow** – The Vulkan backend now has components to extract frames from video, AI masking, native SfM, meshing, and batch processing, accessible from both GUI and CLI.
- **July 22, 2026: Cross-vendor support** – A Vulkan backend has been added, which works on NVIDIA, AMD, and Intel GPUs.
## Download
Binaries for Windows, Linux, and macOS can be downloaded from [Releases page](https://github.com/harry7557558/spirula-studio/releases/). Simply select the one for your platform, download and unzip, and double click to open the GUI.
If you are training on remote/cloud GPUs, you may use the CLI – Run `spirula --help` for details. By default, `spirula train` command will serve a viewer on an HTTP port, one you can forward over ssh and view training progress in your web browser.
## Build from source
To build from source, Spirula Studio provides two backends:
- **Vulkan (Recommended):** The cross-platform and cross-vendor option. Most tested. Works on all major GPUs. Faster to build and produces smaller binary.
- **CUDA:** Legacy option for CUDA-capable NVIDIA GPUs.
Both provide the same training and meshing functionality. CUDA backend may be faster or slower than Vulkan depending on GPU driver, with difference generally within a few percents. Vulkan backend can be slightly more VRAM efficient in some cases.
| Backend | GPU/Vendor Support | Platform Support | Dependencies | Additional Features |
|--------|--------|--------|--------|--------|
| Vulkan | NVIDIA, AMD, Intel, Apple Silicon | Windows, Linux, macOS | Vulkan/MoltenVK, CMake/Ninja | Native support for SfM, frame extraction from videos, and AI masking |
| CUDA | Most NVIDIA GPUs | Windows, Linux | CUDA, CMake/Ninja | - |
Details for building the Vulkan backend
Make sure you have Vulkan SDK installed. On macOS, MoltenVK is automatically fetched by CMake. Clone the repository and run the commands:
### Windows with MSVC:
```bat
cd spirula-studio\
.\build_develop.bat -DSS_BACKEND=vulkan -DSS_ENABLE_PATENTED=ON
```
If it builds successfully, you get `build_vulkan\spirula.exe`.
### Windows with GCC/Clang:
```bat
cd spirula-studio\
cmake -G Ninja -B build_vulkan -DCMAKE_BUILD_TYPE=Release -DSS_BACKEND=vulkan -DSS_ENABLE_PATENTED=ON -DCMAKE_MAKE_PROGRAM=Ninja
cmake --build build_vulkan -j
```
Pass `-DCMAKE_C_COMPILER` and `-DCMAKE_CXX_COMPILER` to the first `cmake` command if needed.
If it builds successfully, you get `build_vulkan\spirula.exe`.
### Linux:
```bash
cd spirula-studio/
bash build_develop.bash -DSS_BACKEND=vulkan -DSS_ENABLE_PATENTED=ON
```
If it builds successfully, you get `build_vulkan/spirula` binary.
### macOS:
```bash
cd spirula-studio/
bash build_develop.bash -DSS_BACKEND=vulkan -DSS_ENABLE_PATENTED=ON
cmake --build build --target macos_app
cmake --build build --target macos_dmg
```
macOS has only the one backend, so it builds into `build/` rather than into a per-backend tree. If it builds successfully, you get `build/spirula` binary similar to Linux. Additionally, it wraps that binary in a double-clickable `build/Spirula Studio.app`, as well as disk image `build/Spirula Studio.dmg`. MoltenVK is statically linked by default and will run on a Mac without dependency installed.
### Notes regarding third-party licensing
`-DSS_ENABLE_PATENTED=ON` enables decoding video on the GPU instead of shelling out to ffmpeg (about 15x faster frame extraction, and without need to install ffmpeg). However, AVC/HEVC bitstream parsers carry third-party patent exposure. If you turn this on, you are responsible for ensuring compliance with local patent laws regarding AVC/HEVC playback.
Masking needs a SAM checkpoint, which the GUI downloads on first use and caches. The checkpoints are Meta's models under Meta's licenses – SAM 2.1 is Apache-2.0, SAM 3 is under Meta's own, non-standard license. They are never bundled, and the GUI shows the terms before fetching anything. On the command line, point `--model` at a file you downloaded yourself.
Details for building the CUDA backend
Make sure you have a recent version of CUDA installed. On Windows, you also need MSVC compiler compatible with your CUDA version. Clone the repository and run the commands:
### Windows:
```bat
cd spirula-studio\
.\build_develop.bat -DSS_BACKEND=cuda
```
If it builds successfully, you get `build_cuda\spirula.exe`.
### Linux:
```bash
cd spirula-studio/
bash build_develop.bash -DSS_BACKEND=cuda
```
If it builds successfully, you get `build_cuda/spirula` binary.
## Gallery
You can find some professional-quality splats trained by Spirula Studio from [Megascapes Library](https://library.getmegascapes.com/) and their [SuperSplat page](https://superspl.at/user/megascapes).
Some splats created by the author of Spirula Studio can also be found on my [SuperSplat page](https://superspl.at/user?id=harry7557558).
## Trivia
Spirula Studio (formerly spirulae-splat) is named after the now-inactive project [spirulae](https://github.com/harry7557558/spirulae), which was named after the [deep-ocean cephalopod mollusk](https://en.wikipedia.org/wiki/Spirula).
Spirula Studio is developed and maintained almost entirely by one person. Issues and PRs welcome – I sometimes respond late, but rest assured that I do review them all.