Abstract
To decide whether the perceived quality of a mesh is influenced by a certain modification such as compression or simplification, a metric for estimating the visual quality of 3D meshes is required. Today, machine learning and deep learning techniques are getting increasingly popular since they present efficient solutions to many complex problems. However, these techniques are not much utilized in the field of 3D shape perception. We propose a novel machine learning-based approach for evaluating the visual quality of 3D static meshes. The novelty of our study lies in incorporating crowdsourcing in a machine learning framework for visual quality evaluation. We deliberate that this is an elegant way since modeling human visual system processes is a tedious task and requires tuning many parameters. We employ crowdsourcing methodology for collecting data of quality evaluations and metric learning for drawing the best parameters that well correlate with the human perception. Experimental validation of the proposed metric reveals a promising correlation between the metric output and human perception. Results of our crowdsourcing experiments are publicly available for the community.
Similar content being viewed by others
References
Abouelaziz, I., El Hassouni, M., Cherifi, H.: No-reference 3D mesh quality assessment based on dihedral angles model and support vector regression. In: International Conference on Image and Signal Processing, pp. 369–377. Springer (2016)
Abouelaziz, I., El Hassouni, M., Cherifi, H.: Blind 3D mesh visual quality assessment using support vector regression. Multimedia Tools and Applications pp. 1–22 (2018)
Alliez, P., Cohen-Steiner, D., Devillers, O., Lévy, B., Desbrun, M.: Anisotropic polygonal remeshing. ACM Trans. Graph. 22, 485–493 (2003)
Bulbul, A., Capin, T., Lavoué, G., Preda, M.: Assessing visual quality of 3-d polygonal models. IEEE Signal Processing Mag. 28(6), 80–90 (2011)
Chetouani, A.: A 3D mesh quality metric based on features fusion. Electron. Imaging 2017(20), 4–8 (2017)
Cignoni, P., Corsini, M., Ranzuglia, G.: Meshlab: an open-source 3D mesh processing system. ERCIM News 73, 45–46 (2008)
Cignoni, P., Rocchini, C., Scopigno, R.: Metro: measuring error on simplified surfaces. In: Computer Graphics Forum, vol. 17, pp. 167–174. Wiley Online Library (1998)
Corsini, M., Gelasca, E., Ebrahimi, T., Barni, M.: Watermarked 3-D mesh quality assessment. IEEE Trans. Multimedia 9(2), 247–256 (2007)
Corsini, M., Larabi, M.C., Lavoué, G., Petřík, O., Váša, L., Wang, K.: Perceptual metrics for static and dynamic triangle meshes. Comput. Graph. Forum 32, 101–125 (2013)
Daly, S.J.: Visible differences predictor: an algorithm for the assessment of image fidelity. In: SPIE/IS&T 1992 Symposium on Electronic Imaging: Science and Technology, pp. 2–15. International Society for Optics and Photonics (1992)
Dong, L., Fang, Y., Lin, W., Seah, H.S.: Perceptual quality assessment for 3D triangle mesh based on curvature. IEEE Trans. Multimedia 17(12), 2174–2184 (2015)
Garces, E., Agarwala, A., Gutierrez, D., Hertzmann, A.: A similarity measure for illustration style. ACM Trans. Graph. 33(4), 93 (2014)
Gingold, Y., Shamir, A., Cohen-Or, D.: Micro perceptual human computation for visual tasks. ACM Trans. Graph. 31(5), 119 (2012)
Heer, J., Bostock, M.: Crowdsourcing graphical perception: using mechanical turk to assess visualization design. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 203–212. ACM (2010)
Karni, Z., Gotsman, C.: Spectral compression of mesh geometry. In: Proceedings of the 27th annual conference on Computer graphics and interactive techniques, pp. 279–286. ACM Press/Addison-Wesley Publishing Co. (2000)
Kleiman, Y., Goldberg, G., Amsterdamer, Y., Cohen-Or, D.: Toward semantic image similarity from crowdsourced clustering. Vis. Comput. 32(6–8), 1045–1055 (2016)
Koenderink, J.J., van Doorn, A.J.: Surface shape and curvature scales. Image Vis. Comput. 10(8), 557–564 (1992)
Koyama, Y., Sakamoto, D., Igarashi, T.: Crowd-powered parameter analysis for visual design exploration. In: Proceedings of the 27th annual ACM symposium on User interface software and technology, pp. 65–74. ACM (2014)
Kulis, B.: Metric learning: a survey. Found. Trends Mach. Learn. 5(4), 287–364 (2012)
Kundu, D., Ghadiyaram, D., Bovik, A.C., Evans, B.L.: No-reference quality assessment of tone-mapped hdr pictures. IEEE Trans. Image Process. 26(6), 2957–2971 (2017)
Lavoué, G.: A local roughness measure for 3D meshes and its application to visual masking. ACM Trans. Appl. Percept. 5(4), 21 (2009)
Lavoué, G.: A multiscale metric for 3D mesh visual quality assessment. Comput. Graph. Forum 30, 1427–1437 (2011)
Lavoué, G., Cheng, I., Basu, A.: Perceptual quality metrics for 3D meshes: towards an optimal multi-attribute computational model. In: Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on, pp. 3271–3276. IEEE (2013)
Lavoué, G., Gelasca, E.D., Dupont, F., Baskurt, A., Ebrahimi, T.: Perceptually driven 3D distance metrics with application to watermarking. In: Optics & Photonics, pp. 63,120L–63,120L. International Society for Optics and Photonics (2006)
Lavoué, G., Mantiuk, R.: Quality assessment in computer graphics. In: Visual Signal Quality Assessment, pp. 243–286. Springer (2015)
Lee, C., Varshney, A., Jacobs, D.: Mesh saliency. In: ACM SIGGRAPH 2005 Papers, pp. 659–666. ACM (2005)
Lin, W., Kuo, C.C.J.: Perceptual visual quality metrics: a survey. J. Vis. Commun. Image Represent. 22(4), 297–312 (2011)
Liu, T., Hertzmann, A., Li, W., Funkhouser, T.: Style compatibility for 3D furniture models. ACM Trans. Graph. 34(4), 85 (2015)
Lun, Z., Kalogerakis, E., Sheffer, A.: Elements of style: learning perceptual shape style similarity. ACM Trans. Graph. 34(4), 84 (2015)
Maglo, A., Lavoué, G., Dupont, F., Hudelot, C.: 3D mesh compression: survey, comparisons, and emerging trends. ACM Comput. Surv. 47(3), 44 (2015)
Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21(12), 4695–4708 (2012)
Nocedal, J., Wright, S.: Numerical optimization. Springer Science & Business Media (2006)
Nouri, A., Charrier, C., Lézoray, O.: 3D blind mesh quality assessment index. Electron. Imaging 2017(20), 9–26 (2017)
Ramanarayanan, G., Ferwerda, J., Walter, B., Bala, K.: Visual equivalence: towards a new standard for image fidelity. In: ACM SIGGRAPH 2007 papers, SIGGRAPH ’07. ACM, New York (2007)
Saad, M.A., Bovik, A.C., Charrier, C.: Blind image quality assessment: a natural scene statistics approach in the dct domain. IEEE Trans. Image Process. 21(8), 3339–3352 (2012)
Saleh, B., Dontcheva, M., Hertzmann, A., Liu, Z.: Learning style similarity for searching infographics. In: Proceedings of the 41st Graphics Interface Conference, pp. 59–64. Canadian Information Processing Society (2015)
Secord, A., Lu, J., Finkelstein, A., Singh, M., Nealen, A.: Perceptual models of viewpoint preference. ACM Trans. Graph. 30(5), 109 (2011)
Sorkine, O., Cohen-Or, D., Toledo, S.: High-pass quantization for mesh encoding. In: Symposium on Geometry Processing, vol. 42 (2003)
Torkhani, F., Wang, K., Chassery, J.M.: A curvature-tensor-based perceptual quality metric for 3D triangular meshes. Mach. Graph. Vis. 23(1–2), 59–82 (2014)
Torkhani, F., Wang, K., Chassery, J.M.: Perceptual quality assessment of 3D dynamic meshes: subjective and objective studies. Signal Process. Image Commun. 31, 185–204 (2015). https://doi.org/10.1016/j.image.2014.12.008
Váša, L., Rus, J.: Dihedral angle mesh error: a fast perception correlated distortion measure for fixed connectivity triangle meshes. Comput. Graph. Forum 31, 1715–1724 (2012)
Wang, K., Lavoué, G., Denis, F., Baskurt, A., He, X.: A benchmark for 3D mesh watermarking. In: Proc. of the IEEE International Conference on Shape Modeling and Applications, pp. 231–235 (2010)
Wang, K., Torkhani, F., Montanvert, A.: A fast roughness-based approach to the assessment of 3D mesh visual quality. Comput. Graph. 36(7), 808–818 (2012)
Yildiz, Z.C., Capin, T.: A perceptual quality metric for dynamic triangle meshes. EURASIP J. Image Video Process. 2017(1), 12 (2017). https://doi.org/10.1186/s13640-016-0157-y
Yumer, M.E., Chaudhuri, S., Hodgins, J.K., Kara, L.B.: Semantic shape editing using deformation handles. ACM Trans. Graph. 34(4), 86 (2015)
Acknowledgements
This work is supported by the Scientific and Technical Research Council of Turkey (TUBITAK). Also, we would like to thank Yeojin Yun for her kind help in setting up the crowdsourcing platform.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yildiz, Z.C., Oztireli, A.C. & Capin, T. A machine learning framework for full-reference 3D shape quality assessment. Vis Comput 36, 127–139 (2020). https://doi.org/10.1007/s00371-018-1592-9
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00371-018-1592-9