File size: 1,586 Bytes
54b65ce 264b752 54b65ce 264b752 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | ---
license: mit
pipeline_tag: image-to-3d
---
# StructSplat: Generalizable 3D Gaussian Splatting from Uncalibrated Sparse Views
This repository contains the pretrained weights for **StructSplat**, a feed-forward and generalizable 3D Gaussian reconstruction framework that operates directly on uncalibrated images without requiring camera parameters.
* **Paper:** [StructSplat: Generalizable 3D Gaussian Splatting from Uncalibrated Sparse Views](https://huggingface.co/papers/2606.28321)
* **Project Page:** [https://structsplat.github.io](https://structsplat.github.io)
* **Code:** [https://github.com/J-C-Zhao/StructSplat](https://github.com/J-C-Zhao/StructSplat)
## Installation & Evaluation
To set up the environment and run training or evaluation, please refer to the instructions in the [GitHub Repository](https://github.com/J-C-Zhao/StructSplat).
### Setup Environment
```bash
conda create -n structsplat python=3.10.19
conda activate structsplat
pip install torch==2.4.0 torchvision==0.19.0 -i https://download.pytorch.org/whl/cu118
pip install -r requirements.txt
```
### Evaluation
Run the following command to evaluate the model:
```bash
python evaluation.py -c config/dl3dv.yaml
```
## Citation
If you find this work useful, please cite the paper:
```bibtex
@inproceedings{zhao2026structsplat,
title={StructSplat: Generalizable 3D Gaussian Splatting from Uncalibrated Sparse Views},
author={Zhao, Jia-Chen and Chen, Beiqi and Chen, Xinyang and Wang, Guangcong and Nie, Liqing},
booktitle={European Conference on Computer Vision},
year={2026}
}
``` |