Comparing Small Visual Differences between Conforming Meshes

Bian, Zhe; Hu, Shi‐Min; Martin, Ralph R.

doi:10.1007/978-3-540-79246-8_5

Cited by 4 publications

(6 citation statements)

References 16 publications

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“…In the ambit of quality evaluation of 3D watermarking algorithms, Lavoué et al [28] proposed a perceptuallyinspired metric called Mesh Structural Distortion Measure (MSDM). Most recently, Bian et al [29], [30] developed another geometry-based perceptual metric based on the strain energy, i.e. a measure of the energy which causes the deformation between the original and the processed mesh.…”

Section: Model-based Perceptual Metricsmentioning

confidence: 99%

“…We have also included two combinations of the Root Mean Square error with the Geometric Laplacian respectively introduced by Karni and Gotsman [24] and Sorkine et al [31]. Finally, we have considered four of the recent model-based perceptual metrics just mentioned: the Mesh Structural Distortion Measure of Lavoué et al [28], the two roughness-based metrics developed by Corsini and Drelie Gelasca et al [27] and the Strain Field-based metric from Bian et al [29], [30]. We do not consider perceptual image-based metrics in our experiments since they are not reliable to predict the perceived visual impairment on 3D models for the reasons just explained at the begin of Section 2.2.…”

Section: Details About the Tested Metricsmentioning

confidence: 99%

“…The strain energy calculation on the mesh is simplified considering that each mesh element (a triangular mesh is assumed) is perturbed along its plane. For the details about the simplification assumptions we refer to the original papers [29], [30]. It 3. http://meshlab.sourceforge.net/ is important to underline that this metric is suitable for small deformations.…”

Section: Strain Field-based Measure (Sf )mentioning

confidence: 99%

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A Comparison of Perceptually-Based Metrics for Objective Evaluation of Geometry Processing

Lavoué

Corsini

2010

IEEE Trans. Multimedia

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Abstract-Recent advances in 3D graphics technologies have led to an increasing use of processing techniques on 3D meshes, such as filtering, compression, watermarking, simplification, deformation and so forth. Since these processes may modify the visual appearance of the 3D objects, several metrics have been introduced to properly drive or evaluate them, from classic geometric ones such as Hausdorff distance, to more complex perceptually-based measures. This paper presents a survey on existing perceptuallybased metrics for visual impairment of 3D objects and provides an extensive comparison between them. In particular, different scenarios which correspond to different perceptual and cognitive mechanisms are analyzed. The objective is twofold: (1) catching the behavior of existing measures to help Perception researchers for designing new 3D metrics and (2) providing a comparison between them to inform and help Computer Graphics researchers for choosing the most accurate tool for the design and the evaluation of their mesh processing algorithms.

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Section: Model-based Perceptual Metricsmentioning

confidence: 99%

Section: Details About the Tested Metricsmentioning

confidence: 99%

Section: Strain Field-based Measure (Sf )mentioning

confidence: 99%

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A Comparison of Perceptually-Based Metrics for Objective Evaluation of Geometry Processing

Lavoué

Corsini

2010

IEEE Trans. Multimedia

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“…We are only showing the results of MSDM and MSDM2 compared with our proposed metric. All other metrics we have evaluated, that is Visual error [KG00], two versions of roughness difference [CGEB07], strain field measure [BHM08], spatial part of STED measure [VS11], PSNR, MSE and versions of Hausdorff distance computed by the Metro tool [CRS98] performed much worse than these three. Our proposed measure has achieved an average Pearson coefficient of 90.7, which clearly outperforms the second best result of MSDM2 with average Pearson coefficient of 83.4.…”

Section: Resultsmentioning

confidence: 99%

“…A comparison method based on strain field has been proposed by Bian et al [BHM08]. The idea is to integrate the strain energy of an imaginary force that has deformed the object.…”

Section: Related Workmentioning

confidence: 99%

Dihedral Angle Mesh Error: a fast perception correlated distortion measure for fixed connectivity triangle meshes

Váša

Rus

2012

Computer Graphics Forum

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In computer graphics, triangle meshes are ubiquitous as a representation of surface models. Processing of this kind of data, such as compression or watermarking, often involves an unwanted distortion of the surface geometry. Advanced processing algorithms are continuously being proposed, aiming at improving performance (compression ratio, watermark robustness and capacity), while minimizing the introduced distortion. In most cases, the final resulting mesh is intended to be viewed by a human observer, and it is therefore necessary to minimise the amount of distortion perceived by the human visual system. However, only recently there have been studies published on subjective experiments in this field, showing that previously used objective error measures exhibit rather poor correlation with the results of subjective experiments. In this paper, we present results of our own large subjective testing aimed at human perception of triangle mesh distortion. We provide an independent confirmation of the previous result by Lavoué et al. that most current metrics perform poorly, with the exception of the MSDM/MSDM2 metrics. We propose a novel metric based on measuring the distortion of dihedral angles, which provides even higher correlation with the results of our experiments and experiments performed by other researchers. Our metric is about two orders of magnitude faster than MSDM/MSDM2, which makes it much more suitable for usage in iterative optimisation algorithms.

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A new mesh visual quality metric using saliency weighting-based pooling strategy

Feng

Wan

et al. 2018

Graphical Models

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Several metrics have been proposed to assess the visual quality of 3D triangular meshes during the last decade. In this paper, we propose a mesh visual quality metric by integrating mesh saliency into mesh visual quality assessment. We use the Tensor-based Perceptual Distance Measure metric to estimate the local distortions for the mesh, and pool local distortions into a quality score using a saliency weighting-based pooling strategy. Three well-known mesh saliency detection methods are used to demonstrate the superiority and effectiveness of our metric. Experimental results show that our metric with any of three saliency maps performs better than state-of-the-art metrics on the LIRIS/EPFL general-purpose database. We generate a synthetic saliency map by assembling salient regions from individual saliency maps. Experimental results reveal that the synthetic saliency map achieves better performance than individual saliency maps, and the performance gain is closely correlated with the similarity between the individual saliency maps.

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Comparing Small Visual Differences between Conforming Meshes

Cited by 4 publications

References 16 publications

A Comparison of Perceptually-Based Metrics for Objective Evaluation of Geometry Processing

A Comparison of Perceptually-Based Metrics for Objective Evaluation of Geometry Processing

Dihedral Angle Mesh Error: a fast perception correlated distortion measure for fixed connectivity triangle meshes

A new mesh visual quality metric using saliency weighting-based pooling strategy

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