# Subjective and Objective Visual Quality Assessment of Textured 3D Meshes

arXiv: https://arxiv.org/abs/2102.03982 · HTML: https://mlsanity.com/paper/2102.03982/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 53 references that are in the map.
- First posted on arXiv 8 Feb 2021.

## Built on by (most cited first, 13 of 13)

- [No-Reference Quality Assessment for 3D Colored Point Cloud and Mesh Models](https://mlsanity.com/paper/2107.02041/) (2021)
- [Confusing Image Quality Assessment: Towards Better Augmented Reality Experience](https://mlsanity.com/paper/2204.04900/) (2022)
- [Evaluating Point Cloud from Moving Camera Videos: A No-Reference Metric](https://mlsanity.com/paper/2208.14085/) (2022)
- [GMS-3DQA: Projection-based Grid Mini-patch Sampling for 3D Model Quality Assessment](https://mlsanity.com/paper/2306.05658/) (2023)
- [TSMD: A Database for Static Color Mesh Quality Assessment Study](https://mlsanity.com/paper/2308.01940/) (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)
- [DHQA-4D: Perceptual Quality Assessment of Dynamic 4D Digital Human](https://mlsanity.com/paper/2510.03874/) (2025)
- [Automatic Testing and Validation of Level of Detail Reductions Through Supervised Learning](https://mlsanity.com/paper/2208.12674/) (2022)
- [HybridMQA: Exploring Geometry-Texture Interactions for Colored Mesh Quality Assessment](https://mlsanity.com/paper/2412.01986/) (2024)
- [Realistic face animation generation from videos](https://mlsanity.com/paper/2103.14984/) (2021)
- [Enhancing Content Representation for AR Image Quality Assessment Using Knowledge Distillation](https://mlsanity.com/paper/2412.06003/) (2024)
- [ARIQA-3DS: A Stereoscopic Image Quality Assessment Dataset for Realistic Augmented Reality](https://mlsanity.com/paper/2604.03112/) (2026)

## Abstract (arXiv)

Objective visual quality assessment of 3D models is a fundamental issue in computer graphics. Quality assessment metrics may allow a wide range of processes to be guided and evaluated, such as level of detail creation, compression, filtering, and so on. Most computer graphics assets are composed of geometric surfaces on which several texture images can be mapped to 11 make the rendering more realistic. While some quality assessment metrics exist for geometric surfaces, almost no research has been conducted on the evaluation of texture-mapped 3D models. In this context, we present a new subjective study to evaluate the perceptual quality of textured meshes, based on a paired comparison protocol. We introduce both texture and geometry distortions on a set of 5 reference models to produce a database of 136 distorted models, evaluated using two rendering protocols. Based on analysis of the results, we propose two new metrics for visual quality assessment of textured mesh, as optimized linear combinations of accurate geometry and texture quality measurements. These proposed perceptual metrics outperform their counterparts in terms of correlation with human opinion. The database, along with the associated subjective scores, will be made publicly available online.