Create README.md

README.md

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+---
+license: apache-2.0
+pipeline_tag: image-to-3d
+---
+# 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.
+
+<!-- *   [📚 Paper](https://huggingface.co/papers/2509.18090) -->
+*   [🌐 Project Page](https://fictionarry.github.io/AmbiSuR-Proj/)
+*   [💻 Code](https://github.com/Fictionarry/AmbiSuR)
+
+## Reconstruction on Tanks and Temples and DTU Datasets
+
+Here we provide the reconstructed meshes of the paper's experiments from GeoSVR.
+
+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}
+}
+```