Abstract Blind or No reference quality evaluation is a challenging issue since it is done without access to the original content. In this work, we propose a method based on deep learning for the mesh visual quality assessment without reference. For a given 3D model, we first compute its mesh saliency. Then, we extract views from the 3D mesh and the corresponding mesh saliency. After that, the views are split into small patches that are filtered using a saliency threshold. Only the salient patches are selected and used as input data. After that, three pre-trained deep convolutional neural networks are employed for feature learning: VGG, AlexNet, and ResNet. Each network is fine-tuned and produces a feature vector. The Compact Multi-linear Pooling (CMP) is used afterward to fuse the retrieved vectors into a global feature representation. Finally, fully connected layers followed by a regression module are used to estimate the quality score. Extensive experiments are executed on four mesh quality datasets and comparisons with existing methods demonstrate the effectiveness of our method in terms of correlation with subjective scores.

中文翻译：

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通过卷积神经网络和紧凑多线性池的集合进行无参考网格视觉质量评估

摘要 盲目或无参考质量评估是一个具有挑战性的问题，因为它是在无法访问原始内容的情况下完成的。在这项工作中，我们提出了一种基于深度学习的网格视觉质量评估方法，无需参考。对于给定的 3D 模型，我们首先计算其网格显着性。然后，我们从 3D 网格和相应的网格显着性中提取视图。之后，视图被分成小块，使用显着性阈值过滤。仅选择显着补丁并将其用作输入数据。之后，使用三个预训练的深度卷积神经网络进行特征学习：VGG、AlexNet 和 ResNet。每个网络都经过微调并产生一个特征向量。之后使用 Compact Multi-linear Pooling (CMP) 将检索到的向量融合为全局特征表示。最后，全连接层后跟回归模块用于估计质量分数。在四个网格质量数据集上进行了大量实验，与现有方法的比较证明了我们的方法在与主观分数的相关性方面的有效性。