TDMD: A Database for Dynamic Color Mesh Subjective and Objective Quality Explorations
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Abstract (arXiv)
Dynamic colored meshes (DCM) are widely used in various applications; however, these meshes may undergo different processes, such as compression or transmission, which can distort them and degrade their quality. To facilitate the development of objective metrics for DCMs and study the influence of typical distortions on their perception, we create the Tencent - dynamic colored mesh database (TDMD) containing eight reference DCM objects with six typical distortions. Using processed video sequences (PVS) derived from the DCM, we have conducted a large-scale subjective experiment that resulted in 303 distorted DCM samples with mean opinion scores, making the TDMD the largest available DCM database to our knowledge. This database enabled us to study the impact of different types of distortion on human perception and offer recommendations for DCM compression and related tasks. Additionally, we have evaluated three types of state-of-the-art objective metrics on the TDMD, including image-based, point-based, and video-based metrics, on the TDMD. Our experimental results highlight the strengths and weaknesses of each metric, and we provide suggestions about the selection of metrics in practical DCM applications. The TDMD will be made publicly available at the following location: https://multimedia.tencent.com/resources/tdmd.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 12 of its 60 references that are in the map. First posted on arXiv 3 Aug 2023.
Builds on
12 of 60 references.
References · 12
- 2018Open3D: A Modern Library for 3D Data Processingcited by 721
- 2020Inferring Point Cloud Quality via Graph Similaritycited by 58
- 2021Perceptual Quality Assessment of Colored 3D Point Cloudscited by 49
- 2021No-Reference Point Cloud Quality Assessment via Domain Adaptationcited by 38
- 2020Reduced Reference Perceptual Quality Model and Application to Rate Control for 3D Point Cloud Compressioncited by 34
- 2021MPED: Quantifying Point Cloud Distortion based on Multiscale Potential Energy Discrepancycited by 28
- 20173D Face Reconstruction with Geometry Details from a Single Imagecited by 27
- 2022Video compression dataset and benchmark of learning-based video-quality metricscited by 27
- 2022Textured Mesh Quality Assessment: Large-Scale Dataset and Deep Learning-based Quality Metriccited by 26
- 2019Point Cloud Rendering after Coding: Impacts on Subjective and Objective Qualitycited by 23
- 2022GPA-Net:No-Reference Point Cloud Quality Assessment with Multi-task Graph Convolutional Networkcited by 22
- 2021Subjective and Objective Visual Quality Assessment of Textured 3D Meshescited by 13
Built on by
Citing papers per year: 2023: 2; 2024: 2; 2025: 0; 2026: 1.
5 papers.
- 2024A Benchmark for Gaussian Splatting Compression and Quality Assessment Studycited by 16
- 2023SJTU-TMQA: A quality assessment database for static mesh with texture mapcited by 10
- 2024Explicit-NeRF-QA: A Quality Assessment Database for Explicit NeRF Model Compressioncited by 8
- 2023GeodesicPSIM: Predicting the Quality of Static Mesh with Texture Map via Geodesic Patch Similaritycited by 4
- 2026N4MC: Neural 4D Mesh Compressioncited by 0
- 2026N4MC: Neural 4D Mesh Compression23 Feb 2026
- 2024A Benchmark for Gaussian Splatting Compression and Quality Assessment Study19 Jul 2024
- 2024Explicit-NeRF-QA: A Quality Assessment Database for Explicit NeRF Model Compression11 Jul 2024
- 2023SJTU-TMQA: A quality assessment database for static mesh with texture map27 Sep 2023
- 2023GeodesicPSIM: Predicting the Quality of Static Mesh with Texture Map via Geodesic Patch Similarity9 Aug 2023