Changed some formatting and removed additional links to arXiv and the project page

README.md

@@ -19,7 +19,7 @@ tags:
 
 -----
 
-> 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:2411.17489](https://arxiv.org/abs/2411.17489) or the [project page](https://nihermann.github.io/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"  
 > Authors: Nicolai Hermann, Jorge Condor, and Piotr Didyk
 
 ### Dataset Description
@@ -27,10 +27,10 @@ The Dataset consists of 36 hand-selected 3D Gaussian Splatting renderings contai
 
 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)