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https://github.com/ldyken53/TVCG-progiso
31 October 2024, 16:40:01 UTC
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Tip revision: 9b4d063dbd517c8db6196fcfba97d7a4442af2c0 authored by Landon Dyken on 11 June 2024, 17:43:45 UTC
Handle all buffer management manually rather than automatically allocating with ONNX
Tip revision: 9b4d063
README.md
# WebGPU Progressive Raycasting with ML Infill
This repo expands https://github.com/Twinklebear/webgpu-prog-iso with multiple optimizations including first pass speculation, 
starting with speculation using larger framebuffers, and ML infill using onnxruntime-web.

## Getting Started

After cloning the repo run

```
npm install
```

Then navigate to the shaders/ folder and run
```
python embed_shaders.py ./glslc.exe ./tint.exe
```

Then back to the top folder run 
```
npm run build
```

Then move the files in "ml-models" along with the "ort-wasm-simd*" files into the built dist/ folder. Then create a folder
"models/bcmc-data" in dist/ and populate with the zfp compressed datasets. 

You can then serve the application from the dist/ folder using 
```
python -m http.server
```

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