A number of full reference and reduced reference methods have been proposed in order to
estimate the perceived visual quality of 3D meshes. However, in most practical situations …

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 …

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 …

This paper proposes a new method for blind mesh visual quality assessment (MVQA) based
on a graph convolutional network. For that, we address the node classification problem to …

No-reference quality assessment is a challenging issue due to the non-existence of any
information related to the reference and the unknown distortion type. The main goal is to …

This paper presents a new method for no reference mesh visual quality assessment using a
convolutional neural network. To do this, we first render 2D images from multiple views of …

Various visual distortions can inevitably affect the 3D meshes during their transmission and
geometrical processing. In most practical cases, blind quality assessment becomes a …

After the sound, 2D images and videos, 3D models represented by polygonal meshes are
the actual emergent content due to the technological advance in terms of 3D acquisition. 3D …

In this paper, we propose a new method for blind mesh visual quality assessment using a
deep learning approach. To do this, we first extract visual representative features by …

Abstract 3D meshes are subject to various visual distortions during their transmission and
geometrical processing. Several works have tried to evaluate the visual quality using either …