Improve dataset card: Add metadata, links, and tags for discoverability (#2)

- Improve dataset card: Add metadata, links, and tags for discoverability (f39a3dbac1dca492e0b3ea9995f842022c3f0f91)
- Changed some formatting and removed additional links to arXiv and the project page (700b4b92e166ff6e124db18db29cbd480ea14eb7)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,20 +1,36 @@
+---
+license: apache-2.0
+language:
+- en
+size_categories:
+- 1K<n<10K
+task_categories:
+- image-segmentation
+tags:
+- 3d-reconstruction
+- artifact-detection
+- image-quality-assessment
+- human-annotation
+---
+
 # <img src="https://www.svgrepo.com/show/510149/puzzle-piece.svg" width="22"/> Puzzle Similarity
 
------
+[Project page](https://nihermann.github.io/puzzlesim/) | [Paper](https://arxiv.org/abs/2411.17489) | [Code](https://github.com/nihermann/PuzzleSim)
 
-> This repository contains the dataset presented in the ICCV 2025 paper "Puzzle Similarity: A Perceptually-guided Cross-Reference Metric for Artifact Detection in 3D Scene Reconstructions" (See arxiv.org/abs/2411.17489 or the [project page](https://nihermann.github.io/))  
-> Authors: Nicolai Hermann, Jorge Condor, and Piotr Didyk  
+-----
 
+> This repository contains the dataset presented in the ICCV 2025 paper "Puzzle Similarity: A Perceptually-guided Cross-Reference Metric for Artifact Detection in 3D Scene Reconstructions"  
+> Authors: Nicolai Hermann, Jorge Condor, and Piotr Didyk
 
 ### Dataset Description
 The Dataset consists of 36 hand-selected 3D Gaussian Splatting renderings containing common reconstruction artefacts, ground truths, human-annotated masks, and a set of reference views of the same scene.
 
 Each mask is an average of 22 binary masks, each created by a different human participant who was asked to annotate areas in the reconstructed images that they perceived as visually degraded, unnatural, or incongruent. The dataset can be used to benchmark No-Reference, Cross-Reference, and Full-Reference image quality metrics for their correlation with human judgment. The naming convention of the data is as follows:
 
-dataset_perc_id_mask.png (grayscale)  
-dataset_perc_id_artifact.png  
-dataset_perc_id_gt.png  
-dataset_perc_refs/  
+- `dataset_perc_id_mask.png` (grayscale)
+- `dataset_perc_id_artifact.png`
+- `dataset_perc_id_gt.png`
+- `dataset_perc_refs/`
 
 The dataset was created by fitting 3DGS to a scene while using a reduced number of training views. We withheld a percentage of views (perc) and added them to the validation dataset, which is found in the *_refs/ directory for each respective sample to act as unseen reference views for Cross-Reference metrics. We fitted the scenes while withholding 60%, 70%, or 80% to get a wider variety and strength of artifacts. (Disclaimer: perc actually refers to proportions, so the possible values are 0.6, 0.7, or 0.8)
 
@@ -26,7 +42,6 @@ The included datasets are a collection from the Mip-NeRF360 [1], Tanks and Templ
 
 [3] Peter Hedman, Julien Philip, True Price, Jan-Michael Frahm, 619 George Drettakis, and Gabriel Brostow. Deep blending for 620 free-viewpoint image-based rendering. ACM Transactions 621 on Graphics, 37(6):1–15, 2018.
 
-
 ### Citation
 If you find this work useful, please consider citing:
 ```bibtex
@@ -39,12 +54,4 @@ If you find this work useful, please consider citing:
       primaryClass={cs.CV},
       url={https://arxiv.org/abs/2411.17489},
 }
-```
-
----
-license: apache-2.0
-language:
-- en
-size_categories:
-- 1K<n<10K
----
+```