research-article

GMS-3DQA: Projection-Based Grid Mini-patch Sampling for 3D Model Quality Assessment

Authors:

Weisi LinAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications and Applications, Volume 20, Issue 6

Article No.: 178, Pages 1 - 19

https://doi.org/10.1145/3643817

Published: 08 March 2024 Publication History

Abstract

Nowadays, most three-dimensional model quality assessment (3DQA) methods have been aimed at improving accuracy. However, little attention has been paid to the computational cost and inference time required for practical applications. Model-based 3DQA methods extract features directly from the 3D models, which are characterized by their high degree of complexity. As a result, many researchers are inclined towards utilizing projection-based 3DQA methods. Nevertheless, previous projection-based 3DQA methods directly extract features from multi-projections to ensure quality prediction accuracy, which calls for more resource consumption and inevitably leads to inefficiency. Thus, in this article, we address this challenge by proposing a no-reference (NR) projection-based Grid Mini-patch Sampling 3D Model Quality Assessment (GMS-3DQA) method. The projection images are rendered from six perpendicular viewpoints of the 3D model to cover sufficient quality information. To reduce redundancy and inference resources, we propose a multi-projection grid mini-patch sampling strategy (MP-GMS), which samples grid mini-patches from the multi-projections and forms the sampled grid mini-patches into one quality mini-patch map (QMM). The Swin-Transformer tiny backbone is then used to extract quality-aware features from the QMMs. The experimental results show that the proposed GMS-3DQA outperforms existing state-of-the-art NR-3DQA methods on the point cloud quality assessment databases for both accuracy and efficiency. The efficiency analysis reveals that the proposed GMS-3DQA requires far less computational resources and inference time than other 3DQA competitors. The code is available at https://github.com/zzc-1998/GMS-3DQA.

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Cited By

Zhang ZSun WZhu YMin XWu WChen YZhai G(2025)Evaluating Point Cloud From Moving Camera Videos: A No-Reference MetricIEEE Transactions on Multimedia10.1109/TMM.2023.334089427(927-939)Online publication date: 2025
https://doi.org/10.1109/TMM.2023.3340894
Sang WSu HLiu QYuan HWang Z(2025)Energy-Adaptive Bitstream-Layer Model for Perceptual Quality Assessment of V-PCC Encoded 3D Point CloudsIEEE Transactions on Image Processing10.1109/TIP.2025.353946534(1285-1296)Online publication date: 2025
https://doi.org/10.1109/TIP.2025.3539465
Li CLu GFeng DWu HZhang ZLiu XZhai GLin WZhang W(2025)MISC: Ultra-Low Bitrate Image Semantic Compression Driven by Large Multimodal ModelIEEE Transactions on Image Processing10.1109/TIP.2024.351587434(335-349)Online publication date: 2025
https://doi.org/10.1109/TIP.2024.3515874
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Index Terms

GMS-3DQA: Projection-Based Grid Mini-patch Sampling for 3D Model Quality Assessment
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
2. Human-centered computing
  1. Visualization
    1. Visualization design and evaluation methods

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 20, Issue 6

June 2024

715 pages

EISSN:1551-6865

DOI:10.1145/3613638

Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

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Publication History

Published: 08 March 2024

Online AM: 01 February 2024

Accepted: 28 January 2024

Revised: 30 November 2023

Received: 20 August 2023

Published in TOMM Volume 20, Issue 6

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NSFC
Fundamental Research Funds for the Central Universities, National Key R&D Program of China
Shanghai Municipal Science and Technology Major Project

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Cited By

Zhang ZSun WZhu YMin XWu WChen YZhai G(2025)Evaluating Point Cloud From Moving Camera Videos: A No-Reference MetricIEEE Transactions on Multimedia10.1109/TMM.2023.334089427(927-939)Online publication date: 2025
https://doi.org/10.1109/TMM.2023.3340894
Sang WSu HLiu QYuan HWang Z(2025)Energy-Adaptive Bitstream-Layer Model for Perceptual Quality Assessment of V-PCC Encoded 3D Point CloudsIEEE Transactions on Image Processing10.1109/TIP.2025.353946534(1285-1296)Online publication date: 2025
https://doi.org/10.1109/TIP.2025.3539465
Li CLu GFeng DWu HZhang ZLiu XZhai GLin WZhang W(2025)MISC: Ultra-Low Bitrate Image Semantic Compression Driven by Large Multimodal ModelIEEE Transactions on Image Processing10.1109/TIP.2024.351587434(335-349)Online publication date: 2025
https://doi.org/10.1109/TIP.2024.3515874
Wu BShang XZhao XShen LAn PMa S(2025)CMDC-PCQA: No-Reference Point Cloud Quality Assessment via a Cross-Modal Deep-Coupling FrameworkIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2025.354178274(1-13)Online publication date: 2025
https://doi.org/10.1109/TIM.2025.3541782
Tious AVigier TRicordel V(2024)New challenges in point cloud visual quality assessment: a systematic reviewFrontiers in Signal Processing10.3389/frsip.2024.14200604Online publication date: 1-Nov-2024
https://doi.org/10.3389/frsip.2024.1420060
Chen PWang SLi Z(2024)Occupancy Map Guided Attributes Artifacts Removal for Video-Based Point Cloud CompressionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/369735120:12(1-20)Online publication date: 26-Nov-2024
https://dl.acm.org/doi/10.1145/3697351
Zhou YZhang ZWen FJia JMin XWang JZhai G(2024)ReLI-QA: A Multidimensional Quality Assessment Dataset for Relighted Human Heads2024 IEEE International Conference on Visual Communications and Image Processing (VCIP)10.1109/VCIP63160.2024.10849906(1-5)Online publication date: 8-Dec-2024
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Chen WJiang QZhou WXu LLin W(2024)Dynamic Hypergraph Convolutional Network for No-Reference Point Cloud Quality AssessmentIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.341005234:10(10479-10493)Online publication date: Oct-2024
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Chai XShao FMu BChen HJiang QHo Y(2024)Plain-PCQA: No-Reference Point Cloud Quality Assessment by Analysis of Plain Visual and Geometrical ComponentsIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.335018034:7(6207-6223)Online publication date: Jul-2024
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