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Currently, great numbers of efforts have been put into improving theeffectiveness of 3D model quality assessment (3DQA) methods. However, littleattention has been paid to the computational costs and inference time, which isalso important for practical applications. Unlike 2D media, 3D models arerepresented by more complicated and irregular digital formats, such as pointcloud and mesh. Thus it is normally difficult to perform an efficient module toextract quality-aware features of 3D models. In this paper, we address thisproblem 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 imagesof EEP-3DQA are randomly sampled from the six perpendicular viewpoints of the3D model and are further spatially downsampled by the grid-mini patch samplingstrategy. Further, the lightweight Swin-Transformer tiny is utilized as thebackbone to extract the quality-aware features. Finally, the proposed EEP-3DQAand EEP-3DQA-t (tiny version) achieve the best performance than the existingstate-of-the-art NR-3DQA methods and even outperforms most full-reference (FR)3DQA methods on the point cloud and mesh quality assessment databases whileconsuming less inference time than the compared 3DQA methods.

EEP-3DQA: Efficient and Effective Projection-based 3D Model Quality Assessment