Apple's SHARP running in the browser via ONNX runtime web
Hi HN, author here. SHARP is Apple's recent single-image 3D Gaussian splatting model (https://arxiv.org/abs/2512.10685). Their reference code is PyTorch + a pretty heavy pipeline; I wanted to see if it could run in a browser with no server hop, so I exported the predictor to ONNX and ran it via onnxruntime-web with the WebGPU EP.What works: drop in an image, get a .ply you can download or preview live, all on your machine — your image never leaves the tab. The model is large (~2.4 GB sidecar) so first load is slow on a cold cache, but inference itself is a few seconds on a recent Mac.Caveats: SHARP's released weights are research-use only (Apple's model license, not the code's). I host the exported ONNX on R2 so thedemo "just works", but you can also export your own from the upstream Apple repo and upload locally.Happy to talk about it in the comments :)
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A browser-based implementation of Apple's SHARP 3D Gaussian splatting model using ONNX runtime for privacy-preserving, client-side 3D reconstruction from a single image.
The pain
The gap
Build angle
Strengths
- Privacy-first approach: all processing happens locally in the browser
- No server infrastructure needed, reducing costs and complexity
- Leverages cutting-edge Apple research and ONNX runtime for accessibility
- Open and extensible: users can upload their own models
Risks
- Large model size (~2.4GB) causes slow initial load and may deter users
- Research-use-only license limits commercial applicability
- Niche developer/researcher audience limits market size
- Performance and UX may suffer on less powerful devices or browsers
- Unclear monetization or product-market fit beyond demo/tool status
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