We provide a large-scale dataset of textured meshes with over 343k stimuli generated from 55 source models quantitatively characterized in terms of geometric, color, and semantic complexity to ensure their diversity. The dataset covers a wide range of compression-based distortions applied on the geometry, texture mapping and texture image. The database can be used to train no-reference quality metrics and develop rate-distortion models for meshes.
From the established dataset, we carefully selected a challenging subset of 3000 stimuli that we annotated in a large-scale subjective experiment in crowdsourcing based on the double stimulus impairment scale (DSIS) method. Over 148k quality scores were collected from 4513 participants. To the best of our knowledge, it is the largest quality assessment dataset of textured meshes associated with subjective scores and Mean Opinion Scores (MOS) to date. This database is valuable for training and benchmarking quality metrics.
Quality scores of the remaining stimuli in the dataset (i.e. those not involved in the subjective experiment) were predicted (Pseudo-MOS) using a quality metric called Graphics-LPIPS, based on deep learning, trained and tested on the subset of annotated stimuli.
This dataset was created at the LIRIS lab, Université de Lyon. It is associated with the following reference. Please cite it, if you use the dataset.
Yana Nehmé, Johanna Delanoy, Florent Dupont, Jean-Philippe Farrugia, Patrick Le Callet, Guillaume Lavoué, Textured mesh quality assessment: Large-scale dataset and deep learning-based quality metric, ACM Transactions on Graphics, Volume 42, Issue 3, Article No. 31, pp 1–20, 2023.
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