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 this https URL

该研究提出了一种新的基于投影的 3D 模型质量评估方法 - GMS-3DQA，通过使用多投影网格小块采样策略减少冗余和推理资源，并使用 Swin-Transformer 微型骨干从采样的网格小块中提取质量感知特征，实验结果表明，该模型相比现有的 NR-3DQA 方法具有更高的评估准确度和更少的计算资源需求。

GMS-3DQA：基于投影的网格小块采样用于 3D 模型质量评估

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

In this work we propose a deep neural network based surrogate model for a
plasma shadowgraph - a technique for visualization of perturbations in a
transparent medium. We are substituting the numerical code by a computationally
cheaper projection based surrogate model that is able to approximate the
electric fields at a given time without computing all preceding electric fields
as required by numerical methods. This means that the projection based
surrogate model allows to recover the solution of the governing 3D partial
differential equation, 3D wave equation, at any point of a given compute domain
and configuration without the need to run a full simulation. This model has
shown a good quality of reconstruction in a problem of interpolation of data
within a narrow range of simulation parameters and can be used for input data
of large size.

本文提出了一种基于深度神经网络的代理模型，用于可视化透明介质中扰动的等离子体暗场图，该投影型代理模型能够在不需要计算所有先前的电场的情况下，近似于在给定时间内的电场。