Datasets:
Tasks:
Image Segmentation
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
< 1K
ArXiv:
License:
| 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" | |
| > 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, (aligned) ground truths, human-annotated masks, and a set of unaligned 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 Hedman, Ricardo Martin-Brualla, and Pratul P. Srinivasan. Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields, 2021. | |
| [2] Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun. Tanks and temples: benchmarking large-scale scene reconstruction. ACM Transactions on Graphics, 36(4):1–13, 2017 | |
| [3] Peter Hedman, Julien Philip, True Price, Jan-Michael Frahm, George Drettakis, and Gabriel Brostow. Deep blending for free-viewpoint image-based rendering. ACM Transactions on Graphics, 37(6):1–15, 2018. | |
| ### Citation | |
| If you find this work useful, please consider citing: | |
| ```bibtex | |
| @InProceedings{Hermann_puzzlesim_iccv25, | |
| author = {Hermann, Nicolai and Condor, Jorge and Didyk, Piotr}, | |
| title = {Puzzle Similarity: A Perceptually-guided Cross-Reference Metric for Artifact Detection in 3D Scene Reconstructions}, | |
| booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, | |
| month = {October}, | |
| year = {2025}, | |
| pages = {28881-28891} | |
| } | |
| ``` |