| --- |
| license: cc-by-nc-4.0 |
| license_link: LICENSE |
| pipeline_tag: image-to-3d |
| tags: |
| - gaussian-splatting |
| - novel-view-synthesis |
| - stereo |
| - depth-estimation |
| - 3d-reconstruction |
| --- |
| |
| # StereoSplat |
|
|
| Model weights for **StereoSplat: Metric-Scale Novel View Synthesis via Stereo-Grounded Gaussian |
| Splatting** (NeurIPS 2026). |
|
|
| [Paper](https://arxiv.org/abs/2610.35902) | |
| [Project Page](https://vladimiryugay.github.io/stereosplat/) | |
| [Code](https://github.com/facebookresearch/stereo-splat) |
|
|
| StereoSplat predicts metric-scale 3D Gaussians from calibrated stereo pairs in a single |
| feed-forward pass, and renders novel views and depth. |
|
|
| ## Files |
|
|
| | File | Content | SHA-256 | |
| |---|---|---| |
| | `stereosplat_v1.safetensors` | Weights; the model config is in the file metadata | `e84556050f65b28367a6bab67606d742db7b4881aa202582d687ad560260184f` | |
| | `stereosplat_v1.json` | Same config as plain JSON | `8c8c57468beafb55fabbea5cd2a72efff9b994a986839f891b27033a8fdff1cb` | |
|
|
| ## FoundationStereo dependency |
|
|
| StereoSplat uses a frozen [FoundationStereo](https://github.com/NVlabs/FoundationStereo) backbone, |
| which is not included here and is subject to NVIDIA's license. Get it separately: |
|
|
| ``` |
| git clone https://github.com/NVlabs/FoundationStereo third_party/FoundationStereo |
| git -C third_party/FoundationStereo checkout 6e8806816b533e4d13ddbb95ffa907b797060a62 |
| ``` |
|
|
| Download the ViT-S model `11-33-40` from the FoundationStereo README and place it as: |
|
|
| ``` |
| checkpoints/foundation_stereo/11-33-40/ |
| cfg.yaml |
| model_best_bp2.pth # sha256 8d7850b9dc68d1366722a02a39745704b1db41471211be6abd93ef463727e6be |
| ``` |
|
|
| ## Usage |
|
|
| Install the code following the [GitHub repository](https://github.com/facebookresearch/stereo-splat), |
| then: |
|
|
| ``` |
| hf download facebook/stereo-splat stereosplat_v1.safetensors --local-dir checkpoints |
| |
| python scripts/demo.py \ |
| --left path/to/left.png --right path/to/right.png \ |
| --intrinsics FX FY CX CY --baseline 0.25 \ |
| --checkpoint checkpoints/stereosplat_v1.safetensors \ |
| --fs-checkpoint-dir checkpoints/foundation_stereo/11-33-40 \ |
| --output-dir outputs/demo |
| ``` |
|
|
| The demo writes the Gaussians (`.ply`, standard 3DGS layout), the input-view depth, and rendered |
| RGB and depth for novel cameras. Rendering needs a CUDA GPU. |
|
|
| In Python: |
|
|
| ```python |
| from huggingface_hub import hf_hub_download |
| from stereosplat.models.build import build_model, load_checkpoint, read_checkpoint |
| |
| path = hf_hub_download("facebook/stereo-splat", "stereosplat_v1.safetensors") |
| config = dict(read_checkpoint(path)["config"], fs_checkpoint_path="checkpoints/foundation_stereo/11-33-40") |
| model = build_model(config) # loads FoundationStereo |
| load_checkpoint(model, path) # loads StereoSplat |
| model = model.cuda().eval() |
| ``` |
|
|
| ## Results |
|
|
| Novel view synthesis at 320 x 640 with [MAC-VO](https://github.com/MAC-VO/MAC-VO) poses (paper |
| protocol: 3 consecutive stereo pairs, first and last as input, middle rendered). |
|
|
| | Benchmark | PSNR | SSIM | LPIPS | AbsRel | delta1 (%) | |
| |---|---|---|---|---|---| |
| | Infinigen | 29.41 | 0.954 | 0.116 | 0.033 | 98.56 | |
| | TartanAir v2 | 26.25 | 0.944 | 0.101 | 0.058 | 94.95 | |
| | KITTI (zero-shot) | 25.20 | 0.941 | 0.101 | 0.044 | 95.98 | |
|
|
| Trained on TartanAir, Infinigen and [XRStereo](https://huggingface.co/datasets/facebook/xrstereo). |
|
|
| ## License |
|
|
| [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/), see [LICENSE](LICENSE). |
| Non-commercial use only. FoundationStereo is subject to NVIDIA's license. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{yugay2026stereosplat, |
| title = {StereoSplat: Metric-Scale Novel View Synthesis via Stereo-Grounded Gaussian Splatting}, |
| author = {Yugay, Vladimir and Rozumny, Denis and Gevers, Theo and Vansteenkiste, Elias and Oswald, Martin R. and Luvizon, Diogo C.}, |
| booktitle = {Advances in Neural Information Processing Systems (NeurIPS)}, |
| year = {2026} |
| } |
| ``` |
|
|
