README.md

---
license: apache-2.0
pipeline_tag: image-to-3d
tags:
- gaussian-splatting
- 3d
- surface-reconstruction
---

# Revisiting Photometric Ambiguity for Accurate Gaussian-Splatting Surface Reconstruction

This repository provides the reconstructed meshes and resources for the paper [Revisiting Photometric Ambiguity for Accurate Gaussian-Splatting Surface Reconstruction](https://huggingface.co/papers/2605.12494).

**Authors**: Jiahe Li, Jiawei Zhang, Xiao Bai, Jin Zheng, Xiaohan Yu, Lin Gu, Gim Hee Lee.

*   [📚 Paper](https://huggingface.co/papers/2605.12494)
*   [🌐 Project Page](https://fictionarry.github.io/AmbiSuR-Proj/)
*   [💻 Code](https://github.com/Fictionarry/AmbiSuR)

## Overview
AmbiSuR is a framework that explores an intrinsic solution upon Gaussian Splatting for photometric ambiguity-robust surface 3D reconstruction. By revisiting built-in primitive-wise ambiguities, the framework introduces a photometric disambiguation constraint and an ambiguity indication module to identify and guide the correction of underconstrained reconstructions, achieving high-performance surface formation in challenging scenarios.

## Reconstruction on Tanks and Temples and DTU Datasets

Here we provide the reconstructed meshes of the paper's experiments from AmbiSuR.

You can browse all the released meshes at:

-   `ambisur-<dataset>-meshes-eval/`: The meshes on DTU/TnT datasets, with strict filtering strategy for evaluation.
-   `ambisur-<dataset>-meshes-vis/`: The meshes on DTU/TnT datasets, with loose filtering strategy for visualization.

Metrics shall be reproduced with the results with postfix of `-eval`.

## Download

```python
from huggingface_hub import snapshot_download
snapshot_download(repo_id="Fictionary/AmbiSuR", cache_dir='./AmbiSuR/results', local_dir ='./AmbiSuR/results')
```
or use Git to clone this repository with LFS.

## Citation
```bibtex
@inproceedings{li2026ambisur,
  title={Revisiting Photometric Ambiguity for Accurate Gaussian-Splatting Surface Reconstruction},
  author={Li, Jiahe and Zhang, Jiawei and Bai, Xiao and Zheng, Jin and Yu, Xiaohan and Gu, Lin and Lee, Gim Hee},
  booktitle={International Conference on Machine Learning},
  year={2026},
  organization={PMLR}
}
```