GitHub - bring-shrubbery/ml-sharp-web: Web playground to create Gaussian Splats using Apple's ml-sharp model.

A browser-based Gaussian splat generator built on top of Apple SHARP. ✨ This project lets you:

upload one image generate Gaussian splats in the browser preview the result download a .ply file

Links

Project repo: bring-shrubbery/ml-sharp-web Follow the author on X: @bringshrubberyy Upstream SHARP repo (Apple): apple/ml-sharp SHARP project page: apple.github.io/ml-sharp SHARP paper: arXiv:2512.10685

Before you start (important license note) Apple's SHARP repository has separate licenses for code and model weights.

SHARP code license: LICENSE SHARP model license: LICENSE_MODEL

If you use Apple's released SHARP checkpoint/weights, you must follow LICENSE_MODEL (research-use restrictions apply). What you need

Bun installed A modern desktop browser (Chrome or Edge recommended) Enough disk space and RAM for the SHARP model (the exported ONNX sidecar is large, ~2.4 GB)

Quick start (run the app) 🚀 1. Star this repo 🤩 If this project helps you, please star it:

bring-shrubbery/ml-sharp-web

2. Install dependencies bun install This also copies ONNX Runtime Web WASM assets into public/ort/ automatically. 3. Start the app bun dev Open the URL shown by Vite (usually http://localhost:5173). 4. Use the app

Upload an image. Click Generate Splat. Preview the result and download the .ply file.

Important model file note (.onnx + .onnx.data) SHARP exports usually produce two files:

sharp_web_predictor.onnx sharp_web_predictor.onnx.data

Both files must be served together from the same folder (for example public/models/). Why this matters:

The .onnx file is only the graph and metadata. The .onnx.data file contains most of the model weights.

For that reason, the app uses the hosted model by default. Uploading only the .onnx file directly in the browser usually will not work because the .onnx.data sidecar is separate.

Export the SHARP model to ONNX (beginner-friendly steps) Everything runs in the browser, but you still need an exported SHARP ONNX model. 1. Clone Apple's SHARP repo (reference code) git clone https://github.com/apple/ml-sharp /tmp/ml-sharp-upstream 2. Prepare a Python environment for export You need Python + SHARP dependencies + ONNX export dependencies. The easiest route is to follow the upstream SHARP setup first, then run this exporter script from this repo. 3. Export the browser predictor ONNX From this repo: python3 scripts/export_sharp_onnx.py \ --sharp-repo /tmp/ml-sharp-upstream \ --output public/models/sharp_web_predictor.onnx If the model is large (it is), the script will also write: public/models/sharp_web_predictor.onnx.data

Optional export flags

--checkpoint /path/to/sharp_2572gikvuh.pt to use a manually downloaded checkpoint --device cuda to export on GPU (if your environment supports it) --opset 20 to change ONNX opset (default is 20)

Static build (optional) If you want a static build instead of running bun dev: bun run build bun run preview Notes:

bun run build copies public/ into dist/, including the model files. If sharp_web_predictor.onnx.data is present, the build output will be very large.

How it works (high level)

React + TypeScript UI (src/) ONNX Runtime Web worker for inference (src/workers/sharpWorker.ts) Browser-side SHARP postprocessing (NDC -> metric gaussian conversion) Browser-side PLY writer In-page preview with @mkkellogg/gaussian-splats-3d

Troubleshooting 🛠️ "expected magic word ... found 3c 21 64 6f" (WASM error) This means a WASM file request returned HTML instead.

Try:

run the app with bun dev (not file://...) restart the dev server after bun install verify these load in your browser:

/ort/ort-wasm-simd-threaded.asyncify.mjs /ort/ort-wasm-simd-threaded.asyncify.wasm

"Failed to load external data file ... sharp_web_predictor.onnx.data" This means the ONNX sidecar file is missing or not served correctly. Check:

public/models/sharp_web_predictor.onnx public/models/sharp_web_predictor.onnx.data The app can reach the hosted model files in your deployment/browser environment

The app runs, but generation is very slow or crashes SHARP is large and browser inference is heavy. Try:

Chrome or Edge (desktop) smaller Max gaussians in the UI closing other memory-heavy tabs/apps waiting longer on first run (model + runtime initialization can take time)

Tech stack

Bun React TypeScript Vite ONNX Runtime Web GaussianSplats3D viewer

Project status Working prototype / experimental. 🧪 The app runs end-to-end in the browser, but performance and compatibility depend heavily on browser WebGPU/WASM support and your machine's available memory.