A fast roughness-based approach to the assessment of 3D mesh visual quality

Wang, Kai; Torkhani, Fakhri; Montanvert, Annick

doi:10.1016/j.cag.2012.06.004

Cited by 106 publications

(98 citation statements)

References 45 publications

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“…Similar relationships can also be found in the Lion, Pegasus, and Elephant models. The concept of local final richness (LFR) [14] is used in this paper to represent mesh geometric texture richness. To compute LFR, we first calculate the Laplacian of the Gaussian curvature to give the local richness (LR).…”

Section: Geometric Texture Richness Measurementioning

confidence: 99%

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Relief generation from 3D scenes guided by geometric texture richness

et al. 2018

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Typically, relief generation from an input 3D scene is limited to either bas-relief or high-relief modeling. This paper presents a novel unified scheme for synthesizing reliefs guided by the geometric texture richness of 3D scenes; it can generate both basand high-reliefs. The type of relief and compression coefficient can be specified according to the user's artistic needs. We use an energy minimization function to obtain the surface reliefs, which contains a geometry preservation term and an edge constraint term. An edge relief measure determined by geometric texture richness and edge z-depth is utilized to achieve a balance between these two terms.During relief generation, the geometry preservation term keeps local surface detail in the original scenes, while the edge constraint term maintains regions of the original models with rich geometric texture. Elsewhere, in highreliefs, the edge constraint term also preserves depth discontinuities in the higher parts of the original scenes. The energy function can be discretized to obtain a sparse linear system. The reliefs are obtained by solving it by an iterative process. Finally, we apply non-linear compression to the relief to meet the user's artistic needs. Experimental results show the method's effectiveness for generating both bas-and high-reliefs for complex 3D scenes in a unified manner.

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Section: Geometric Texture Richness Measurementioning

confidence: 99%

“…The geometric texture richness is formulated in terms of variation of curvature, which is related to surface fairness and visual quality [14]. Using geometric texture richness has the advantage of preserving fine details in highly textured regions of complex shapes, whilst those in smoother regions are suppressed during relief generation.…”

Section: Introductionmentioning

confidence: 99%

Relief generation from 3D scenes guided by geometric texture richness

et al. 2018

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“…Another metric developed for 3D mesh quality assessment is called FMPD which is based on local roughness estimated from Gaussian curvature [48]. Torkhani and colleagues [44] propose another metric (TPDM) based on curvature tensor difference of the meshes to be compared.…”

Section: Model-based Perceptual Metricsmentioning

confidence: 99%

A perceptual quality metric for dynamic triangle meshes

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2017

J Image Video Proc.

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A measure for assessing the quality of a 3D mesh is necessary in order to determine whether an operation on the mesh, such as watermarking or compression, affects the perceived quality. The studies on this field are limited when compared to the studies for 2D. In this work, we aim a full-reference perceptual quality metric for animated meshes to predict the visibility of local distortions on the mesh surface. The proposed visual quality metric is independent of connectivity and material attributes. Thus, it is not associated to a specific application and can be used for evaluating the effect of an arbitrary mesh processing method. We use a bottom-up approach incorporating both the spatial and temporal sensitivity of the human visual system. In this approach, the mesh sequences go through a pipeline which models the contrast sensitivity and channel decomposition mechanisms of the HVS. As the output of the method, a 3D probability map representing the visibility of distortions is generated. We have validated our method by a formal user experiment and obtained a promising correlation between the user responses and the proposed metric. Finally, we provide a dataset consisting of subjective user evaluation of the quality of public animation datasets.

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“…Gaussian curvature based local roughness measure was calculated by Wang et al [27]. A visual similarity metric Tensor based Perceptual Distortion Measure (TPDM) is introduced by Torkhani et al [21] which is based on the measurement of a distance between curvature tensors of the two triangle meshes under comparison.…”

Section: Curvature Based Measuresmentioning

confidence: 99%

“…Laplacian of discrete Gaussian curvatures are used by Wang et al [27] to calculate the local roughness and normalized surface integrals of the local roughness is used to calculate the global roughness. They termed measure to be Fast Mesh Perceptual Distance (FMPD) which is faster than other visual similarity indexes.…”

Section: Curvature Based Measuresmentioning

confidence: 99%