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…
In plain words
SHARP is Apple's single-image 3D Gaussian splatting model ported to run directly in web browsers using ONNX runtime and WebGPU. Users upload an image and receive a downloadable 3D point cloud file or live preview, with all processing happening locally on their machine. The tool is designed for developers and 3D enthusiasts who want to convert 2D images to 3D models without server uploads, though the initial load time is lengthy due to the large 2.4GB model file.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
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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