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
• Paper: https://arxiv.org/abs/2602.06032
• Project Page: https://davidshavin4.github.io/Splat-and-Distill/
• Blog Post: Medium | Splat and Distill

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:

@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)}, 
}