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katolikov/
yonosplat-vknn

Name: yonosplat-vknn
Availability: InStock

vknn

onnx

3d-gaussian-splatting

yonosplat

vulkan

mit

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YoNoSplat encoder — ONNX + vknn (`.vxm`)

The YoNoSplat feed-forward 3D-Gaussian-Splatting encoder (8 context views), exported to ONNX and compiled to a fp16 vknn .vxm for on-device Vulkan inference.

file	what
`yonosplat_encoder.onnx`	encoder graph, opset 17 (weights external in `weights.bin`)
`weights.bin`	fp32 weights for the ONNX (~3.6 GB)
`encoder8_fp16.vxm`	compiled fp16 vknn model, ready to run (~2.9 GB)
`image8.npy`, `intr8.npy`	sample real 8-frame input (RealEstate10K scene)
`*_gold.npy`	onnxruntime goldens for the six outputs

Input: image [1,8,3,224,224], intrinsics [1,8,3,3]. Output: means [1,401408,3], covariances, harmonics, opacities, rotations, scales (401408 = 8 views × 224×224 Gaussians).

Use

pip install huggingface_hub
python benchmark/scripts/fetch_model.py --repo katolikov/yonosplat-vknn --out benchmark/models
# then run + validate on device with the benchmark tool (see vknn/benchmark/USAGE.md)
python benchmark/run.py run benchmark/configs/yonosplat.json

On a current flagship phone GPU (Vulkan, fp16) the compiled encoder matches an onnxruntime golden at cosine ≥ 0.9997 on all six outputs.

Provenance & license

Architecture + checkpoint: YoNoSplat by Botao Ye et al. (code MIT; checkpoint re10k_224x224_ctx2to32). The ONNX here is a faithful export (analytic 3×3 inverse, Newton–Schulz SVD, baked DINOv2 pos-embed) validated cos = 1.0 against the original.
The weights are trained on RealEstate10K, whose dataset terms are research / non-commercial — use accordingly.
This export/compilation is provided under MIT, with attribution to the original authors.

katolikov/yonosplat-vknn

YoNoSplat encoder — ONNX + vknn (.vxm)

Use

Provenance & license

katolikov/
yonosplat-vknn

YoNoSplat encoder — ONNX + vknn (`.vxm`)