Link paper and GitHub repository in dataset card (#1)

- Link paper and GitHub repository in dataset card (e199dcd639523db5807b31c0cb26d9e59f3896e5)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,5 +1,4 @@
 ---
-pretty_name: "MD-3K: MultiDepth-3K Benchmark"
 language:
 - en
 license: other
@@ -7,6 +6,7 @@ size_categories:
 - 1K<n<10K
 task_categories:
 - depth-estimation
+pretty_name: 'MD-3K: MultiDepth-3K Benchmark'
 tags:
 - depth-estimation
 - monocular-depth-estimation
@@ -25,10 +25,10 @@ MD-3K is a real-world diagnostic benchmark for probing **geometric ambiguity** i
 
 This dataset accompanies the ECCV 2026 paper:
 
-> **One Scene, Two Depths: Probing Geometric Ambiguity in Monocular Foundation Models**  
+> **[One Scene, Two Depths: Probing Geometric Ambiguity in Monocular Foundation Models](https://arxiv.org/abs/2606.29600)**  
 > Xiaohao Xu, Feng Xue, Xiang Li, Haowei Li, Shusheng Yang, Tianyi Zhang, Matthew Johnson-Roberson, Xiaonan Huang.
 
-Code repository: `https://github.com/Xiaohao-Xu/Ambiguity-in-Space`
+Code repository: [Xiaohao-Xu/Ambiguity-in-Space](https://github.com/Xiaohao-Xu/Ambiguity-in-Space)
 
 ## Release files
 
@@ -195,11 +195,9 @@ This is not a per-image oracle and should not be described as automatic test-tim
 
 The evaluation and analysis code is maintained in the GitHub repository:
 
-```text
-https://github.com/Xiaohao-Xu/Ambiguity-in-Space
-```
+[https://github.com/Xiaohao-Xu/Ambiguity-in-Space](https://github.com/Xiaohao-Xu/Ambiguity-in-Space)
 
-Laplacian Visual Prompting (LVP) is a deterministic, training-free input transformation. It has no learned parameters and no checkpoint, so it is not released here as a Hugging Face model. The intended use is to apply the transform to the input image, map the result back to a standard RGB image representation, and pass it through the same depth-model processor used for the original RGB input.
+Laplacian Visual Prompting (LVP) is a deterministic, training-free spectral input transformation. It has no learned parameters and no checkpoint, so it is not released here as a Hugging Face model. The intended use is to apply the transform to the input image, map the result back to a standard RGB image representation, and pass it through the same depth-model processor used for the original RGB input.
 
 ## Recommended use
 
@@ -227,13 +225,13 @@ If you use MD-3K, please cite:
 ```bibtex
 @inproceedings{xu2026onescenetwodepths,
   title={One Scene, Two Depths: Probing Geometric Ambiguity in Monocular Foundation Models},
-  author={Xu, Xiaohao and Xue, Feng and Li, Xiang and Li, Haowei and Yang, Shusheng and Zhang, Tianyi and Johnson-Roberson, Matthew and Huang, Xiaonan},
+  author={Xu, Xiaohao and Xue, Feng and Li, Xiang and Li, Haowei and Yang, Shusheng and Zhang, Tianyi and Matthew Johnson-Roberson, Matthew and Huang, Xiaonan},
   booktitle={European Conference on Computer Vision (ECCV)},
   year={2026}
 }
 ```
 
-Also, the segmentation labels are aourced from GDD dataset. So please cite:
+Also, the segmentation labels are sourced from the GDD dataset. So please cite:
 ```bibtex
 @inproceedings{Mei_2020_CVPR,
     author = {Mei, Haiyang and Yang, Xin and Wang, Yang and Liu, Yuanyuan and He, Shengfeng and Zhang, Qiang and Wei, Xiaopeng and Lau, Rynson W.H.},