No-reference mesh visual quality assessment via ensemble of convolutional neural networks and compact multi-linear pooling

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.