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README.md

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+# <img src="https://www.svgrepo.com/show/510149/puzzle-piece.svg" width="22"/> Puzzle Similarity
+
+-----
+
+> 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  
+
+
+### 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/  
+
+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)
+
+The included datasets are a collection from the Mip-NeRF360 [1], Tanks and Temples [2], and Deep Blending [3] datasets; thus, the ground truths are copies from their data.
+
+[1] Jonathan T. Barron, Ben Mildenhall, Matthew Tancik, Peter 594 Hedman, Ricardo Martin-Brualla, and Pratul P. Srinivasan. 595 Mip-NeRF: A Multiscale Representation for Anti-Aliasing 596 Neural Radiance Fields, 2021.
+
+[2] Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen 656 Koltun. Tanks and temples: benchmarking large-scale scene 657 reconstruction. ACM Transactions on Graphics, 36(4):1–13, 658 2017
+
+[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
+@misc{hermann2024puzzlesim,
+      title={Puzzle Similarity: A Perceptually-guided Cross-Reference Metric for Artifact Detection in 3D Scene Reconstructions},
+      author={Nicolai Hermann and Jorge Condor and Piotr Didyk},
+      year={2024},
+      eprint={2411.17489},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2411.17489},
+}
+```
+
 ---
 license: mit
 language: