metadata
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.
🌐 Links
- Project page: https://naamapearl.github.io/learn2splat/
- Code: https://github.com/autonomousvision/learn2splat
- Hugging Face: https://huggingface.co/autonomousvision/learn2splat
⚙️ 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.
📦 Checkpoints
This repository includes pretrained weights for:
- Learn2SplatSparse: sparse-view reconstruction
- Learn2SplatDense: dense-view reconstruction
See MODEL_ZOO.md for details.
📚 Citation
If you find this project useful, please consider citing:
@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}
}