Currently, great numbers of efforts have been put into improving the effectiveness of 3D model quality assessment (3DQA) methods. However, little attention has been paid to the computational costs and inference time, which is also important for practical applications. Unlike 2D media, 3D models are represented by more complicated and irregular digital formats, such as point cloud and mesh. Thus it is normally difficult to perform an efficient module to extract quality-aware features of 3D models. In this paper, we address this problem from the aspect of projection-based 3DQA and develop a no-reference (NR) \underline{E}fficient and \underline{E}ffective \underline{P}rojection-based \underline{3D} Model \underline{Q}uality \underline{A}ssessment (\textbf{EEP-3DQA}) method. The input projection images of EEP-3DQA are randomly sampled from the six perpendicular viewpoints of the 3D model and are further spatially downsampled by the grid-mini patch sampling strategy. Further, the lightweight Swin-Transformer tiny is utilized as the backbone to extract the quality-aware features. Finally, the proposed EEP-3DQA and EEP-3DQA-t (tiny version) achieve the best performance than the existing state-of-the-art NR-3DQA methods and even outperforms most full-reference (FR) 3DQA methods on the point cloud and mesh quality assessment databases while consuming less inference time than the compared 3DQA methods.

翻译：当前，大量研究致力于提升三维模型质量评估（3DQA）方法的有效性。然而，对实际应用中同样重要的计算成本和推理时间的关注却较少。与二维媒体不同，三维模型以点云、网格等更为复杂且非规则的数字格式表示，因此通常难以设计高效模块来提取三维模型的质量感知特征。本文从基于投影的3DQA角度出发解决该问题，提出一种无参考（NR）的**高效且有效的基于投影的三维模型质量评估（EEP-3DQA）**方法。EEP-3DQA的输入投影图像从三维模型的六个垂直视点随机采样，并通过网格迷你分块采样策略进一步进行空间下采样。此外，采用轻量级Swin-Transformer tiny作为主干网络提取质量感知特征。最终，所提出的EEP-3DQA和EEP-3DQA-t（轻量版）在点云及网格质量评估数据库上不仅达到优于现有最先进NR-3DQA方法的性能，甚至超越多数全参考（FR）3DQA方法，同时其推理时间少于对比的3DQA方法。