3DGCQA: A Quality Assessment Database for 3D AI-Generated Contents
Yingjie Zhou, Zicheng Zhang, Farong Wen, Jun Jia, Yanwei Jiang,, Xiaohong Liu, Xiongkuo Min, Guangtao Zhai

TL;DR
This paper introduces the 3DGCQA dataset for assessing the quality of AI-generated 3D content, highlighting common distortions and evaluating existing quality algorithms to guide future improvements.
Contribution
The paper presents a new comprehensive dataset for 3D AI-generated content quality assessment, including diverse generation methods, distortion categories, and evaluation results.
Findings
Subjective ratings vary significantly across methods.
Existing quality assessment algorithms show limited performance.
The dataset is publicly available for future research.
Abstract
Although 3D generated content (3DGC) offers advantages in reducing production costs and accelerating design timelines, its quality often falls short when compared to 3D professionally generated content. Common quality issues frequently affect 3DGC, highlighting the importance of timely and effective quality assessment. Such evaluations not only ensure a higher standard of 3DGCs for end-users but also provide critical insights for advancing generative technologies. To address existing gaps in this domain, this paper introduces a novel 3DGC quality assessment dataset, 3DGCQA, built using 7 representative Text-to-3D generation methods. During the dataset's construction, 50 fixed prompts are utilized to generate contents across all methods, resulting in the creation of 313 textured meshes that constitute the 3DGCQA dataset. The visualization intuitively reveals the presence of 6 common…
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Taxonomy
Topics3D Shape Modeling and Analysis · Image Processing and 3D Reconstruction · Medical Imaging and Analysis
