Convolutional Neural Network for Blind Mesh Visual Quality Assessment Using 3D Visual Saliency
In this work, we propose a convolutional neural network (CNN) framework to estimate the perceived visual quality of 3D meshes without having access to the reference. The proposed CNN architecture is fed by small patches selected carefully according to their level of saliency. To do so, the visual saliency of the 3D mesh is computed, then we render 2D projections from the 3D mesh and its corresponding 3D saliency map. Afterward, the obtained views are split to obtain 2D small patches that pass through a saliency filter to select the most relevant patches. Experiments are conducted on two MVQ assessment databases, and the results show that the trained CNN achieves good rates in terms of corre…
Mesh Visual Quality based on the combination of convolutional neural networks
Blind quality assessment is a challenging issue since the evaluation is done without access to the reference nor any information about the distortion. In this work, we propose an objective blind method for the visual quality assessment of 3D meshes. The method estimates the perceived visual quality using only information from the distorted mesh to feed pre-trained deep convolutional neural networks. The input data is prepared by rendering 2D views from the 3D mesh and the corresponding saliency map. The views are split into small patches of fixed size that are filtered using a saliency threshold. Only the salient patches are selected as input data. After that, three pre-trained deep convolu…
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 pro…
Combination Of Handcrafted And Deep Learning-Based Features For 3d Mesh Quality Assessment
We propose in this paper a novel objective method to evaluate the perceived visual quality of 3D meshes. The proposed method in no-reference, it relies only on the distorted mesh for the quality estimation. It is based on a pre-trained convolutional neural network (i.e VGG to extract features from the distorted mesh) and handcrafted features extracted directly from the 3D mesh (i.e curvature and dihedral angle). A General Regression Neural Network (GRNN) is used to learn the statistical parameters of the feature vectors and estimate the quality score. Experimental results from for subjective databases (LIRIS masking, LIRIS/EPFL generalpurpose, UWB compression and LEETA simplification) and c…