free-splatter.cpp — GGUF weights
GGUF conversions of TencentARC/FreeSplatter for free-splatter.cpp, a small C/C++ (ggml) inference engine that runs the FreeSplatter neural-network front-half on CPU or a Vulkan GPU — no camera poses, no Python.
Given N uncalibrated images it returns, for every input pixel, a 3D Gaussian (position · spherical-harmonic colour · opacity · scale · rotation) that a Gaussian-splatting viewer can render. The rasterizer and PnP pose solver are out of scope — these weights cover the patch tokenizer → multi-view transformer → per-pixel Gaussian head.
Files
| file | variant | precision | size | notes |
|---|---|---|---|---|
freesplatter-scene-f16.gguf | scene | f16 | ~596 MB | recommended; 2 overlapping views of a scene |
freesplatter-scene-f32.gguf | scene | f32 | ~1.2 GB | full precision (reference / CPU) |
freesplatter-object-f16.gguf | object | f16 | ~596 MB | 3–4 views around one object |
All variants share the same transformer backbone; they differ only in the head
config (gaussian_channels, sh_residual, use_2dgs) and what they were trained
on. The GGUF carries those as free-splatter.* KV metadata, so the engine
configures itself from the file.
Usage
# build the engine (see the repo README), then:
free_splatter-cli --device vulkan --splat scene.splat \
freesplatter-scene-f16.gguf view1.jpg view2.jpg
Each view is center-cropped and resized to 512×512. The output .splat (antimatter15
format) opens in the bundled WebGL viewer or any Gaussian-splatting viewer; pass
--out result.f32 instead for the raw [N, 512, 512, gaussian_channels] float32
Gaussian tensor. There is also a drop-photos-in-the-browser web app and a
multi-scene demo-video renderer — see the project repo.
How these were made
Converted with scripts/convert.py
directly from the upstream FreeSplatter .safetensors checkpoints (needs only
torch + safetensors + gguf). The converter maps the transformer.* weights
of pieces 1–3 and fails loudly on any unmapped or missing tensor — no weight is
silently dropped.
License & attribution
Apache-2.0. These are derivative weights of TencentARC/FreeSplatter (Apache-2.0); all credit for the model goes to the FreeSplatter authors. The GGUF packaging and the inference engine are part of free-splatter.cpp.