Objective Quality Assessment for Three-Dimensional Meshes

Yaoyao Lin; Mei Yu; Zhouyan He; Gangyi Jiang

doi:10.3788/LOP202158.0200003

[1] Li H A, Zhang M, Yu K P et al. R3MR: region growing based 3D mesh reconstruction for big data platform[J]. IEEE Access, 8, 91740-91750(2020). http://www.researchgate.net/publication/341303621_R3MR_Region_Growing_based_3D_Mesh_Reconstruction_for_Big_Data_Platform

[2] Yavuz E, Yazici R. A dynamic neural network model for accelerating preliminary parameterization of 3D triangular mesh surfaces[J]. Neural Computing and Applications, 31, 3691-3701(2019). http://link.springer.com/article/10.1007/s00521-017-3332-x

[3] Arvanitis G, Lalos A S, Moustakas K. Saliency mapping for processing 3D meshes in industrial modeling applications[C]//2019 IEEE 17th International Conference on Industrial Informatics (INDIN), July 22-25, 2019, Helsinki, Finland., 683-686(2019).

[4] Nasiri F, Bidgoli N M, Payan F et al. A geometry-aware framework for compressing 3D mesh textures[C]//ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 12-17, 2019, Brighton, United Ki, 4015-4019(2019).

[5] Li J T, Cheng X J, Yang Z X et al. Curvature-grading-based compression for point cloud data[J]. Laser & Optoelectronics Progress, 56, 142801(2019).

[6] Xian Y Z, Fan Y X, Huang Y et al. Mesh simplification with appearance-driven optimizations[J]. IEEE Access, 8, 165769-165778(2020). http://www.researchgate.net/publication/340671332_Mesh_Simplification_With_Appearance-Driven_Optimizations

[7] Mouhamed M R, Soliman M M, Darwish A A et al. A robust 3D mesh watermarking approach based on genetic algorithm[C]//2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS), December 8-10, 2019, Cairo, Egypt., 408-413(2019).

[8] Tu D W, Lan H, Zhang X. Digital three-dimensional reconstruction technology of cultural relics[J]. Laser & Optoelectronics Progress, 56, 191504(2019).

[9] Hu Y W, Wang J J, Fan Y Y et al. LiDAR-based three-dimensional modeling and volume calculation for space objects[J]. Chinese Journal of Lasers, 47, 0510001(2020).

[10] Guo J J, Vidal V, Cheng I et al. Subjective and objective visual quality assessment of textured 3D meshes[J]. ACM Transactions on Applied Perception, 14, 1-20(2017).

[11] Abouelaziz I, El Hassouni M, Cherifi H. Blind 3D mesh visual quality assessment using support vector regression[J]. Multimedia Tools and Applications, 77, 24365-24386(2018).

[12] Narwaria M, Lin W S. McLoughlin I V, et al. Nonintrusive quality assessment of noise suppressed speech with mel-filtered energies and support vector regression[J]. IEEE Transactions on Audio, Speech, and Language Processing, 20, 1217-1232(2012).

[13] Falk T H, Chan W Y. Single-ended speech quality measurement using machine learning methods[J]. IEEE Transactions on Audio, Speech, and Language Processing, 14, 1935-1947(2006). http://ieeexplore.ieee.org/document/1709883/

[14] Lavoué G, Gelasca E D, Dupont F et al. Perceptually driven 3D distance metrics with application to watermarking[J]. Proceedings of SPIE, 6312, 63120L(2006).

[15] Lavoué G. A local roughness measure for 3D meshes and its application to visual masking[J]. ACM Transactions on Applied Perception, 5, 21(2009).

[16] Lavoué G, Larabi M C, Váša L. On the efficiency of image metrics for evaluating the visual quality of 3D models[J]. IEEE Transactions on Visualization and Computer Graphics, 22, 1987-1999(2016).

[17] Wang Z, Bovik A C, Sheikh H R et al. Image quality assessment: From error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 13, 600-612(2004).

[18] Sheikh H R, Bovik A C. Image information and visual quality[J]. IEEE Transactions on Image Processing, 15, 430-444(2006).

[19] Daly S J. Visible differences predictor: An algorithm for the assessment of image fidelity[J]. Proceedings of SPIE, 1666, 2-15(1992).

[20] AspertN, Santa-CruzD, EbrahimiT. MESH: Measuring errors between surfaces using the Hausdorff distance[C]//Proceedings of IEEE International Conference on Multimedia and Expo, August 26-29, 2002, Lausanne, Switzerland. New York: IEEE Press, 2002: 705- 708.

[21] Cignoni P, Rocchini C, Scopigno R. Metro: Measuring error on simplified surfaces[J]. Computer Graphics Forum, 17, 167-174(1998).

[22] Karni Z, Gotsman C. Spectral compression of mesh geometry. [C]//Proceedings of the 27th annual conference on Computer graphics and interactive techniques, ACM: New York, 279-286(2000).

[23] Sorkine O, Cohen-Or D, Toldeo S. High-pass quantization for mesh encoding[J]. Eurographics Symposium on Geometry Processing, 42-51(2003).

[24] Lavoué G, Corsini M. A comparison of perceptually-based metrics for objective evaluation of geometry processing[J]. IEEE Transactions on Multimedia, 12, 636-649(2010).

[25] Váša L, Rus J. Dihedral angle mesh error: A fast perception correlated distortion measure for fixed connectivity triangle meshes[J]. Computer Graphics Forum, 31, 1715-1724(2012).

[26] Torkhani F, Wang K, Chassery J M. A curvature-tensor-based perceptual quality metric for 3D triangular meshes[J]. Machine Graphics and Vision, 23, 1-25(2014).

[27] Dong L, Fang Y M, Lin W S et al. Perceptual quality assessment for 3D triangle mesh based on curvature[J]. IEEE Transactions on Multimedia, 17, 2174-2184(2015).

[28] Corsini M, Gelasca E D, Ebrahimi T et al. Watermarked 3-D mesh quality assessment[J]. IEEE Transactions on Multimedia, 9, 247-256(2007). http://ieeexplore.ieee.org/document/4066988

[29] Gelasca E D, Ebrahimi T, Corsini M et al. Objective evaluation of the perceptual quality of 3D watermarking[C]//IEEE International Conference on Image Processing 2005, September 14, 2005, Genova, Italy.(2005).

[30] Wang K, Torkhani F, Montanvert A. A fast roughness-based approach to the assessment of 3D mesh visual quality[J]. Computers & Graphics, 36, 808-818(2012).

[31] Abouelaziz I, Omari M, Hassouni M E et al. Reduced reference 3D mesh quality assessment based on statistical models[C]//2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), November 23-27, 2015, B, 170-177(2015).

[32] Abouelaziz I, El Hassouni M, Cherifi H. No-reference 3D mesh quality assessment based on dihedral angles model and support vector regression. [C]//Image and Signal Processing, Berlin: Springer, 9680, 369-377(2016).

[33] Abouelaziz I, Hassouni M E, Cherifi H. A convolutional neural network framework for blind mesh visual quality assessment[C]//2017 IEEE International Conference on Image Processing (ICIP), September 17-20, 2017, Beijing, China., 755-759(2017).

[34] Lin Y Y, Yu M, Chen K et al. Blind mesh quality assessment method based on concave, convex and structural features analyses[C]//2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), July 8-12, 2019, Shang, 282-287(2019).

[35] Lin Y Y, Yu M, Chen K et al. Blind mesh assessment based on graph spectral entropy and spatial features[J]. Entropy, 22, 190(2020).

[36] Teke O, Vaidyanathan P P. Uncertainty principles and sparse eigenvectors of graphs[J]. IEEE Transactions on Signal Processing, 65, 5406-5420(2017).

[37] Dong X W, Thanou D, Frossard P et al. Learning Laplacian matrix in smooth graph signal representations[J]. IEEE Transactions on Signal Processing, 64, 6160-6173(2016).

[38] Shuman D I, Narang S K, Frossard P et al. The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains[J]. IEEE Signal Processing Magazine, 30, 83-98(2013).

[39] Chindanur N B, Sure P. Low-dimensional models for traffic data processing using graph Fourier transform[J]. Computing in Science & Engineering, 20, 24-37(2018).

[40] Wu Z H, Pan S R, Chen F W et al. A comprehensive survey on graph neural networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 8386, 1-21(2020). http://ieeexplore.ieee.org/document/9046288