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| license: mit |
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| # Model Card for Splat and Distill (SnD) |
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| **Splat and Distill (SnD)** is a framework that imparts 3D awareness into 2D Vision Foundation Models (VFMs) by augmenting a teacher network with a feed-forward 3D reconstruction pipeline. It uses 3D Gaussian Splatting (3DGS) to supervise a student model with geometrically consistent features across novel views. |
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| ## Model Details |
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| ### Model Description |
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| SnD bridges the gap between 2D representation and 3D understanding. It lifts 2D features from a teacher model into a 3D feature field using a feed-forward reconstruction model. These features are then "splatted" onto target views to provide a 3D-consistent supervisory signal for the student. |
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| - **Developed by:** David Shavin, Sagie Benaim |
| - **Model type:** 3D-Aware Vision Foundation Model (Distillation Framework) |
| - **Conference:** ICLR 2026 |
| - **License:** MIT |
| - **Finetuned from model:** DINOv2 |
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| ### Model Sources |
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| - **Repository:** [https://github.com/davidshavin4/Splat-and-Distill](https://github.com/davidshavin4/Splat-and-Distill) |
| - **Paper:** [https://arxiv.org/abs/2602.06032](https://arxiv.org/abs/2602.06032) |
| - **Project Page:** [https://davidshavin4.github.io/Splat-and-Distill/](https://davidshavin4.github.io/Splat-and-Distill/) |
| - **Blog Post:** [Medium | Splat and Distill](https://medium.com/@davidshavin4/splat-and-distill-augmenting-teachers-with-feed-forward-3d-reconstruction-for-3d-aware-1f2c5e778399) |
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| ## Uses |
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| ### Direct Use |
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| This model provides 3D-aware semantic features. There are two primary versions available depending on your downstream application: |
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| * **With Blending:** Optimized for **single-view dense estimation tasks**. Use this version for tasks like semantic segmentation, depth estimation, and surface normal estimation. |
| * **Without Blending:** Optimized for tasks requiring **multi-view correspondence**. Use this version for geometric matching or tasks that rely on consistent feature tracking across different perspectives. |
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| ## Bias, Risks, and Limitations |
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| * **Data Bias:** The model was trained using the **ScanNet++** dataset. Consequently, the performance and geometric priors are primarily representative of indoor scene distributions found within that dataset. |
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| ## Citation |
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| **BibTeX:** |
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| ```bibtex |
| @misc{shavin2026splatdistillaugmentingteachers, |
| title={Splat and Distill: Augmenting Teachers with Feed-Forward 3D Reconstruction For 3D-Aware Distillation}, |
| author={David Shavin and Sagie Benaim}, |
| year={2026}, |
| eprint={2602.06032}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV}, |
| url={[https://arxiv.org/abs/2602.06032](https://arxiv.org/abs/2602.06032)}, |
| } |
