| --- |
| license: other |
| license_name: apple-amlr |
| license_link: LICENSE |
| tags: |
| - gaussian-splatting |
| - 3d-reconstruction |
| - onnx |
| - tortuise |
| pipeline_tag: image-to-3d |
| --- |
| |
| # SHARP ONNX — Apple's Single-Image 3D Gaussian Splatting |
|
|
| ONNX export of [Apple's SHARP model](https://github.com/apple/ml-sharp) for use with [tortuise](https://github.com/buildoak/tortuise), a terminal-native 3D Gaussian Splatting viewer. |
|
|
| ## Files |
|
|
| | File | Size | Description | |
| |------|------|-------------| |
| | `sharp.onnx` | 1.9 MB | Model structure (ONNX graph) | |
| | `sharp.onnx.data` | 2.6 GB | Model weights (external data) | |
|
|
| Both files are required. The model exceeds protobuf's 2GB limit, so weights are stored separately. |
|
|
| ## Usage |
|
|
| These files are automatically downloaded by tortuise when you run: |
|
|
| ```bash |
| cargo install tortuise --features sharp |
| tortuise photo.jpg |
| ``` |
|
|
| Or manually place both files in `~/.tortuise/models/`. |
|
|
| ## Model Details |
|
|
| - **Architecture:** DINOv2 ViT-Large encoder + Sliding Pyramid Network + DPT decoders |
| - **Parameters:** 702M (340M trainable) |
| - **Input:** Single RGB image (resized to 1536×1536 internally) |
| - **Output:** ~1.2M 3D Gaussians (positions, scales, rotations, colors, opacities) |
| - **ONNX opset:** 17 |
| - **Source checkpoint:** `sharp_2572gikvuh.pt` from [apple/Sharp](https://huggingface.co/apple/Sharp) |
|
|
| ## License |
|
|
| The model weights are licensed under the [Apple Machine Learning Research Model License](LICENSE). This is a **research-only, non-commercial** license. See the LICENSE file for full terms. |
|
|
| This ONNX conversion is a format transformation of Apple's original PyTorch checkpoint. No architectural modifications were made. |
|
|
| ## Attribution |
|
|
| Based on Apple SHARP model. Copyright (C) 2025 Apple Inc. Licensed under the Apple Machine Learning Research Model License Agreement. |
|
|
| Paper: [SHARP: Monocular View Synthesis in Less Than a Second](https://arxiv.org/abs/2512.10685) |
|
|
