Nowadays, most 3D model quality assessment (3DQA) methods have been aimed at improving performance. 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 paper, we address this challenge by proposing a no-reference (NR) projection-based \textit{\underline{G}rid \underline{M}ini-patch \underline{S}ampling \underline{3D} Model \underline{Q}uality \underline{A}ssessment (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. The efficiency analysis reveals that the proposed GMS-3DQA requires far less computational resources and inference time than other 3DQA competitors. The code will be available at https://github.com/zzc-1998/GMS-3DQA.

翻译：目前，大多数三维模型质量评估方法聚焦于性能提升，却鲜有关注实际应用中的计算成本与推理时间。基于模型的三维质量评估方法直接从三维模型中提取特征，这类方法复杂度极高。因此，研究者更倾向于采用基于投影的三维质量评估方法。然而，以往基于投影的方法需从多视角投影中直接提取特征以确保质量预测精度，这导致资源消耗更大且不可避免效率低下。为此，本文提出一种无参考（NR）的投影式网格迷你块采样三维模型质量评估方法（GMS-3DQA）。该方法从三维模型的六个正交视角渲染投影图像以覆盖充足的质量信息。为减少冗余与推理资源消耗，提出多投影网格迷你块采样策略（MP-GMS），该策略从多视角投影中采集网格迷你块，并将其整合为一张质量迷你块图（QMM）。进而采用Swin-Transformer微小骨干网络从QMM中提取质量感知特征。实验结果表明，在点云质量评估数据库上，所提出的GMS-3DQA方法优于现有最优无参考三维质量评估方法。效率分析揭示，GMS-3DQA所需计算资源与推理时间远少于其他三维质量评估对比方法。代码将发布于 https://github.com/zzc-1998/GMS-3DQA。