  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.
