# Textured Mesh Quality Assessment: Large-Scale Dataset and Deep Learning-based Quality Metric

arXiv: https://arxiv.org/abs/2202.02397 · HTML: https://mlsanity.com/paper/2202.02397/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2022.

## Built on by (most cited first, 26 of 26)

- [Advancing Zero-Shot Digital Human Quality Assessment through Text-Prompted Evaluation](https://mlsanity.com/paper/2307.02808/) (2023)
- [Quality Assessment in the Era of Large Models: A Survey](https://mlsanity.com/paper/2409.00031/) (2024)
- [GMS-3DQA: Projection-based Grid Mini-patch Sampling for 3D Model Quality Assessment](https://mlsanity.com/paper/2306.05658/) (2023)
- [Objaverse++: Curated 3D Object Dataset with Quality Annotations](https://mlsanity.com/paper/2504.07334/) (2025)
- [Perceptual Quality Assessment for Digital Human Heads](https://mlsanity.com/paper/2209.09489/) (2022)
- [TSMD: A Database for Static Color Mesh Quality Assessment Study](https://mlsanity.com/paper/2308.01940/) (2023)
- [SJTU-TMQA: A quality assessment database for static mesh with texture map](https://mlsanity.com/paper/2309.15675/) (2023)
- [DDH-QA: A Dynamic Digital Humans Quality Assessment Database](https://mlsanity.com/paper/2212.12734/) (2022)
- [EEP-3DQA: Efficient and Effective Projection-based 3D Model Quality Assessment](https://mlsanity.com/paper/2302.08715/) (2023)
- [A No-Reference Quality Assessment Method for Digital Human Head](https://mlsanity.com/paper/2310.16732/) (2023)
- [TDMD: A Database for Dynamic Color Mesh Subjective and Objective Quality Explorations](https://mlsanity.com/paper/2308.01499/) (2023)
- [GeodesicPSIM: Predicting the Quality of Static Mesh with Texture Map via Geodesic Patch Similarity](https://mlsanity.com/paper/2308.04928/) (2023)
- [MUGSQA: Novel Multi-Uncertainty-Based Gaussian Splatting Quality Assessment Method, Dataset, and Benchmarks](https://mlsanity.com/paper/2511.06830/) (2025)
- [Screen-based 3D Subjective Experiment Software](https://mlsanity.com/paper/2308.03698/) (2023)
- [GeoScaler: Geometry and Rendering-Aware Downsampling of 3D Mesh Textures](https://mlsanity.com/paper/2311.16581/) (2023)
- [DHQA-4D: Perceptual Quality Assessment of Dynamic 4D Digital Human](https://mlsanity.com/paper/2510.03874/) (2025)
- [Perceptual Crack Detection for Rendered 3D Textured Meshes](https://mlsanity.com/paper/2405.06143/) (2024)
- [HybridMQA: Exploring Geometry-Texture Interactions for Colored Mesh Quality Assessment](https://mlsanity.com/paper/2412.01986/) (2024)
- [Mesh Mamba: A Unified State Space Model for Saliency Prediction in Non-Textured and Textured Meshes](https://mlsanity.com/paper/2504.01466/) (2025)
- [DM-OSVP++: One-Shot View Planning Using 3D Diffusion Models for Active RGB-Based Object Reconstruction](https://mlsanity.com/paper/2504.11674/) (2025)
- [Quality assessment of 3D human animation: Subjective and objective evaluation](https://mlsanity.com/paper/2505.23301/) (2025)
- [Exploring Instruction Data Quality for Explainable Image Quality Assessment](https://mlsanity.com/paper/2510.03880/) (2025)
- [3D2M Dataset: A 3-Dimension diverse Mesh Dataset](https://mlsanity.com/paper/2410.07415/) (2024)
- [Textured mesh Quality Assessment using Geometry and Color Field Similarity](https://mlsanity.com/paper/2505.10824/) (2025)
- [SRAM: Shape-Realism Alignment Metric for No Reference 3D Shape Evaluation](https://mlsanity.com/paper/2512.01373/) (2025)
- [Textured Geometry Evaluation: Perceptual 3D Textured Shape Metric via 3D Latent-Geometry Network](https://mlsanity.com/paper/2512.01380/) (2025)

## Abstract (arXiv)

Over the past decade, 3D graphics have become highly detailed to mimic the real world, exploding their size and complexity. Certain applications and device constraints necessitate their simplification and/or lossy compression, which can degrade their visual quality. Thus, to ensure the best Quality of Experience (QoE), it is important to evaluate the visual quality to accurately drive the compression and find the right compromise between visual quality and data size. In this work, we focus on subjective and objective quality assessment of textured 3D meshes. We first establish a large-scale dataset, which includes 55 source models quantitatively characterized in terms of geometric, color, and semantic complexity, and corrupted by combinations of 5 types of compression-based distortions applied on the geometry, texture mapping and texture image of the meshes. This dataset contains over 343k distorted stimuli. We propose an approach to select a challenging subset of 3000 stimuli for which we collected 148929 quality judgments from over 4500 participants in a large-scale crowdsourced subjective experiment. Leveraging our subject-rated dataset, a learning-based quality metric for 3D graphics was proposed. Our metric demonstrates state-of-the-art results on our dataset of textured meshes and on a dataset of distorted meshes with vertex colors. Finally, we present an application of our metric and dataset to explore the influence of distortion interactions and content characteristics on the perceived quality of compressed textured meshes.