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README.md

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-license: mit
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+---
+license: mit
+---
+
+# Model Card for Splat and Distill (SnD)
+
+**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.
+
+
+
+## Model Details
+
+### Model Description
+
+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.
+
+- **Developed by:** David Shavin, Sagie Benaim
+- **Model type:** 3D-Aware Vision Foundation Model (Distillation Framework)
+- **Conference:** ICLR 2026
+- **License:** MIT
+- **Finetuned from model:** DINOv2
+
+### Model Sources
+
+- **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)
+
+## Uses
+
+### Direct Use
+
+This model provides 3D-aware semantic features. There are two primary versions available depending on your downstream application:
+
+* **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.
+
+## Bias, Risks, and Limitations
+
+* **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.
+
+## Citation
+
+**BibTeX:**
+
+```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)}, 
+}