Image-to-3D
ONNX
Depth Pro
onnxruntime
gaussian-splatting
monocular-view-synthesis
novel-view-synthesis
sharp
oku3d
fp16
opset21
stereo
Instructions to use Jens-Duttke/Sharp-ONNX-HighPerf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Depth Pro
How to use Jens-Duttke/Sharp-ONNX-HighPerf with Depth Pro:
# Download checkpoint pip install huggingface-hub huggingface-cli download --local-dir checkpoints Jens-Duttke/Sharp-ONNX-HighPerf
import depth_pro # Load model and preprocessing transform model, transform = depth_pro.create_model_and_transforms() model.eval() # Load and preprocess an image. image, _, f_px = depth_pro.load_rgb("example.png") image = transform(image) # Run inference. prediction = model.infer(image, f_px=f_px) # Results: 1. Depth in meters depth = prediction["depth"] # Results: 2. Focal length in pixels focallength_px = prediction["focallength_px"] - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- f38db9e12952824bbb6e6129b7da55e7e0bea9472cb796691a325c7949549b14
- Size of remote file:
- 1.99 MB
- SHA256:
- 1776325487872ea2d561342c8777e4ea130f718ffb1420352183aa88332f8684
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