Improve model card and add tags

Hi! I'm Niels from the community science team at Hugging Face.

This PR improves the model card for AmbiSuR. I've added descriptive tags to the metadata to make the work more discoverable, included the list of authors, and added a brief summary of the method based on the paper's abstract. I have also ensured the links to the paper, code, and project page are clearly presented.

README.md

@@ -1,15 +1,25 @@
 ---
 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.
+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.
@@ -17,7 +27,6 @@ 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`.