---
license: cc-by-4.0
tags:
- 3d-reconstruction
- gaussian-splatting
- learned-optimizer
- computer-vision
- view-synthesis
- pytorch
---

# 🫧 Learn2Splat: Learned Optimizer for 3D Gaussian Splatting

**Learn2Splat** is a meta-learned optimizer for **3D Gaussian Splatting (3DGS)** that replaces hand-designed optimizers (e.g., Adam/SGD) with a learned update rule.

It improves early convergence speed while remaining stable over long optimization horizons without requiring learning-rate schedules or time-step conditioning.

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## 🌐 Links

-   Project page: https://naamapearl.github.io/learn2splat/
-   Code: https://github.com/autonomousvision/learn2splat
-   Hugging Face: https://huggingface.co/autonomousvision/learn2splat

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## ⚙️ Overview

Learn2Splat learns to optimize Gaussian scene representations by directly predicting structured parameter updates.

Key properties:

-   Learned optimizer for 3D Gaussian Splatting
-   Faster early convergence compared to standard optimizers
-   Stable long-horizon optimization without LR schedules
-   Zero-shot generalization to unseen scenes and resolutions

The model is trained across many scenes and applied without fine-tuning at test time.

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## 📦 Checkpoints

This repository includes pretrained weights for:

-   Learn2SplatSparse: sparse-view reconstruction
-   Learn2SplatDense: dense-view reconstruction

See [MODEL_ZOO.md](https://huggingface.co/autonomousvision/learn2splat/blob/main/MODEL_ZOO.md) for details.

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## 📚 Citation

If you find this project useful, please consider citing:

```bibtex
@article{pearl2026learn2splat,
  title   = {Learn2Splat: Extending the Horizon of Learned 3DGS Optimization},
  author  = {Pearl, Naama and Esposito, Stefano and Xu, Haofei and Peleg, Amit and
             Gschoßmann, Patricia and Porzi, Lorenzo and Kontschieder, Peter and
             Pons-Moll, Gerard and Geiger, Andreas},
  journal = {arXiv preprint arXiv:2605.15760},
  year    = {2026}
}
```