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OPTIMISED MODELS FOR AR/VR BY USING GEOMETRIC COMPLEXITY METRICS TO CONTROL TESSELLATION

Published online by Cambridge University Press:  19 June 2023

Maximilian Peter Dammann ,
Wolfgang Steger  and
Kristin Paetzold-Byhain
Maximilian Peter Dammann*
Affiliation:
Technische Universität Dresden, Chair of Virtual Product Development
Wolfgang Steger
Affiliation:
Technische Universität Dresden, Chair of Virtual Product Development
Kristin Paetzold-Byhain
Affiliation:
Technische Universität Dresden, Chair of Virtual Product Development
*
Dammann, Maximilian Peter, Technische Universität Dresden, Germany, maximilian_peter.dammann@tu-dresden.de

Abstract

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AR/VR applications are a valuable tool in product design and lifecycle. But the integration of AR/VR is not seamless, as CAD models need to be prepared for the AR/VR applications. One necessary data transformation is the tessellation of the analytically described geometry. To ensure the usability, visual quality and evaluability of the AR/VR application, time consuming optimisation is needed depending on the product complexity and the performance of the target device.

Widespread approaches to this problem are based on iterative mesh decimation. This approach ignores the varying importance of geometries and the required visual quality in engineering applications. Our predictive approach is an alternative that enables optimisation without iterative process steps on the tessellated geometry.

The contribution presents an approach that uses surface-based prediction and enables predictions of the perceived visual quality of the geometries. This contains the investigation of different geometric complexity metrics gathered from literature as basis for prediction models. The approach is implemented in a geometry preparation tool and the results are compared with other approaches.

Keywords

Virtual realityVisualisationMachine learningOptimisation
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 2855 - 2864
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

References

Abouelaziz, I., Omari, M., Hassouni, M.E. and Cherifi, H. (2015), “Reduced Reference 3D Mesh Quality Assessment Based on Statistical Models”, in Yetongnon, K., Dipanda, A. and Chbeir, R. (Eds.), 11th International Conference on Signal-Image Technology and Internet-Based Systems, IEEE, Piscataway, NJ, pp. 170–177. http://doi.org/10.1109/SITIS.2015.129.Google Scholar
Baker, T.J. (2005), “Mesh generation: Art or science?”, Progress in Aerospace Sciences, Vol. 41 No. 1, pp. 29–63. http://doi.org/10.1016/j.paerosci.2005.02.002.Google Scholar
Bulbul, A., Capin, T., Lavoue, G. and Preda, M. (2011), “Assessing Visual Quality of 3-D Polygonal Models”, IEEE Signal Processing Magazine, Vol. 28 No. 6, pp. 80–90. http://doi.org/10.1109/MSP.2011.942466.CrossRefGoogle Scholar
Cheng, S.-W., Shewchuk, J. and Dey, T.K. (2013), Delaunay mesh generation, Chapman & Hall/CRC computer and information science series, 0th ed., Taylor & Francis, Boca Raton, Fla. http://doi.org/10.1201/b12987.Google Scholar
Corsini, M., Gelasca, E.D., Ebrahimi, T. and Barni, M. (2007), “Watermarked 3-D Mesh Quality Assessment”, IEEE Transactions on Multimedia, Vol. 9 No. 2, pp. 247–256. http://doi.org/10.1109/TMM.2006.886261.CrossRefGoogle Scholar
Corsini, M., Larabi, M.C., Lavoué, G., Petřík, O., Váša, L. and Wang, K. (2013), “Perceptual Metrics for Static and Dynamic Triangle Meshes”, Computer Graphics Forum, Vol. 32 No. 1, pp. 101–125.CrossRefGoogle Scholar
Dammann, M.P., Steger, W. and Paetzold, K. (2022a), “A Predictive Approach to Geometry Preparation for AR/VR Applications”, in Volume 2: 42nd Computers and Information in Engineering Conference (CIE), American Society of Mechanical Engineers. http://doi.org/10.1115/DETC2022-88321.CrossRefGoogle Scholar
Dammann, M.P., Steger, W. and Stelzer, R. (2022b), “Automated and Adaptive Geometry Preparation for AR/VR-Applications”, Journal of Computing and Information Science in Engineering, Vol. 22 No. 3. http://doi.org/10.1115/1.4053327.CrossRefGoogle Scholar
Denkena, B., Schürmeyer, J.T., Kaddour, R. and Böß, V. (2011), “Assessing mould costs analysing manufacturing processes of cavities”, The International Journal of Advanced Manufacturing Technology, Vol. 56 No. 9-12, pp. 943–949. http://doi.org/10.1007/s00170-011-3265-y.CrossRefGoogle Scholar
Dong, L., Fang, Y., Lin, W. and Seah, H.S. (2015), “Perceptual Quality Assessment for 3D Triangle Mesh Based on Curvature”, IEEE Transactions on Multimedia, Vol. 17 No. 12, pp. 2174–2184. http://doi.org/10.1109/TMM.2015.2484221.CrossRefGoogle Scholar
Feng, X., Wan, W., Da Xu, R.Y., Chen, H., Li, P. and Sánchez, J.A. (2018), “A perceptual quality metric for 3D triangle meshes based on spatial pooling”, Frontiers of Computer Science, Vol. 12 No. 4, pp. 798–812. http://doi.org/10.1007/s11704-017-6328-x.CrossRefGoogle Scholar
Freeman, I.J., Salmon, J.L. and Coburn, J.Q. (2017), “CAD Integration in Virtual Reality Design Reviews for Improved Engineering Model Interaction”, in Proceedings of the ASME International Mechanical Engineering Congress and Exposition - 2016, The American Society of Mechanical Engineers, New York, N.Y. http://doi.org/10.1115/IMECE2016-66948.CrossRefGoogle Scholar
Graf, H., Brunetti, G. and Stork, A. (2002), “A Methodology Supporting the Preparation of 3D-CAD Data for Design Reviews in VR”, DS 30: Proceedings of DESIGN 2002, the 7th International Design Conference, Dubrovnik, pp. 489–496.Google Scholar
Guo, J., Ding, F., Jia, X. and Yan, D.-M. (2019), “Automatic and high-quality surface mesh generation for CAD models”, Computer-Aided Design, Vol. 109, pp. 49–59. http://doi.org/10.1016/j.cad.2018.12.005.CrossRefGoogle Scholar
Han, Y.-S., Lee, J., Lee, J., Lee, W. and Lee, K. (2019), “3D CAD data extraction and conversion for application of augmented/virtual reality to the construction of ships and offshore structures”, International Journal of Computer Integrated Manufacturing, Vol. 32 No. 7, pp. 658–668. http://doi.org/10.1080/0951192X.2019.1599440.CrossRefGoogle Scholar
Ito, Y., Shih, A.M., Erukala, A.K., Soni, B.K., Chernikov, A., Chrisochoides, N.P. and Nakahashi, K. (2007), “Parallel unstructured mesh generation by an advancing front method”, Mathematics and Computers in Simulation, Vol. 75 No. 5-6, pp. 200–209. http://doi.org/10.1016/j.matcom.2006.12.008.CrossRefGoogle Scholar
Ito, Y., Shih, A.M. and Soni, B.K. (2009), “Octree-based reasonable-quality hexahedral mesh generation using a new set of refinement templates”, International Journal for Numerical Methods in Engineering, Vol. 77 No. 13, pp. 1809–1833. http://doi.org/10.1002/nme.2470.CrossRefGoogle Scholar
Johnson, M.D., Valverde, L.M. and Thomison, W.D. (2018), “An investigation and evaluation of computer-aided design model complexity metrics”, Computer-Aided Design and Applications, Vol. 15 No. 1, pp. 61–75.CrossRefGoogle Scholar
Joshi, D. and Ravi, B. (2010), “Quantifying the Shape Complexity of Cast Parts”, Computer-Aided Design and Applications, 7(5), 685–700, Computer-Aided Design and Applications, Vol. 7 No. 5, pp. 685–700. http://doi.org/10.3722/CADAPS.2010.685-700.CrossRefGoogle Scholar
Koch, S., Matveev, A., Jiang, Z., Williams, F., Artemov, A., Burnaev, E., Alexa, M., Zorin, D. and Panozzo, D. (2019), “ABC: A Big CAD Model Dataset for Geometric Deep Learning”, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Piscataway, NJ, pp. 9593–9603. http://doi.org/10.1109/CVPR.2019.00983.CrossRefGoogle Scholar
Laug, P. and Borouchaki, H. (2011), “High Quality Geometric Meshing of CAD Surfaces”, in Quadros, W.R. (Ed.), Proceedings of the 20th International Meshing Roundtable, Springer, Berlin, pp. 63–80.CrossRefGoogle Scholar
Lavoue, G. and Corsini, M. (2010), “A Comparison of Perceptually-Based Metrics for Objective Evaluation of Geometry Processing”, IEEE Transactions on Multimedia, Vol. 12 No. 7, pp. 636–649. http://doi.org/10.1109/TMM.2010.2060475.CrossRefGoogle Scholar
Lavoué, G. (2011), “A Multiscale Metric for 3D Mesh Visual Quality Assessment”, Computer Graphics Forum, Vol. 30 No. 5, pp. 1427–1437. http://doi.org/10.1111/j.1467-8659.2011.02017.x.CrossRefGoogle Scholar
Lavoué, G., Cheng, I. and Basu, A. (2013), “Perceptual Quality Metrics for 3D Meshes: Towards an Optimal Multi-attribute Computational Model”. http://doi.org/10.1109/SMC.2013.557.CrossRefGoogle Scholar
Liang, X. and Zhang, Y. (2014), “An octree-based dual contouring method for triangular and tetrahedral mesh generation with guaranteed angle range”, Engineering with Computers, Vol. 30 No. 2, pp. 211–222. http://doi.org/10.1007/s00366-013-0328-8.CrossRefGoogle Scholar
Lorenz, M., Spranger, M., Riedel, T., Pürzel, F., Wittstock, V. and Klimant, P. (2016), “CAD to VR – A Methodology for the Automated Conversion of Kinematic CAD Models to Virtual Reality”, Procedia CIRP, Vol. 41, pp. 358–363. http://doi.org/10.1016/j.procir.2015.12.115.CrossRefGoogle Scholar
M Muzahid, A.A., Wan, W. and Feng, X. (2018), “Perceptual Quality Evaluation of 3D Triangle Mesh: A Technical Review”, in ICALIP 2018, IEEE, Piscataway, NJ, pp. 266–272. http://doi.org/10.1109/ICALIP.2018.8455210.CrossRefGoogle Scholar
Maréchal, L. (2009), “Advances in Octree-Based All-Hexahedral Mesh Generation: Handling Sharp Features”, in Clark, B.W. (Ed.), Proceedings of the 18th International Meshing Roundtable, Springer, Berlin, pp. 65–84.CrossRefGoogle Scholar
Montaño Moreno, J.J., Palmer Pol, A., Sesé Abad, A. and Cajal Blasco, B. (2013), “Using the R-MAPE index as a resistant measure of forecast accuracy”, Psicothema, Vol. 25 No. 4, pp. 500–506. http://doi.org/10.7334/psicothema2013.23.Google ScholarPubMed
Nguyen, H., Burkardt, J., Gunzburger, M., Ju, L. and Saka, Y. (2009), “Constrained CVT meshes and a comparison of triangular mesh generators”, Computational Geometry, Vol. 42 No. 1, pp. 1–19. http://doi.org/10.1016/j.comgeo.2008.04.002.CrossRefGoogle Scholar
Okano, A., Matsumoto, T. and Kato, T. (2019), “Curved Surface Shape Generation Method Based on “Complexity” Index”, in 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), IEEE, pp. 1110–1114. http://doi.org/10.1109/SMC.2019.8914066.CrossRefGoogle Scholar
Page, D.L., Koschan, A.F., Sukumar, S.R., Roui-Abidi, B. and Abidi, M.A. (2003), “Shape analysis algorithm based on information theory”, Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference on, Vol. 1, I-229-32. http://doi.org/10.1109/ICIP.2003.1246940.CrossRefGoogle Scholar
Panozzo, D., Puppo, E., Tarini, M., Pietroni, N. and Cignoni, P. (2011), “Automatic construction of quad-based subdivision surfaces using Fitmaps”, IEEE Transactions on Visualization and Computer Graphics, Vol. 17 No. 10, pp. 1510–1520. http://doi.org/10.1109/TVCG.2011.28.CrossRefGoogle ScholarPubMed
Rossignac, J. (2005), “Shape complexity”, The Visual Computer, Vol. 21 No. 12, pp. 985–996. http://doi.org/10.1007/s00371-005-0362-7.CrossRefGoogle Scholar
Salonen, T., Sa¨a¨ski, J., Woodward, C., Korkalo, O., Marstio, I. and Rainio, K. (2009), “Data Pipeline From CAD to AR Based Assembly Instructions”, in Proceedings of the ASME/AFM World Conference on Innovative Virtual Reality - 2009, ASME, New York, NY, pp. 165–168. http://doi.org/10.1115/WINVR2009-705.CrossRefGoogle Scholar
Santos, B., Rodrigues, N., Costa, P. and Coelho, A. (2021), “Integration of CAD Models into Game Engines”, VISIGRAPP 2021 - 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications.CrossRefGoogle Scholar
Sukumar, Sreenivas R., Page, David, Gribok, Andrei V., Koschan, Andreas and Abidi, Mongi A., “Shape Measure for Identifying Perceptually Informative Parts of 3D Objects”, in Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06), pp. 679–686. http://doi.org/10.1109/3DPVT.2006.127.CrossRefGoogle Scholar
Sukumar, S.R., Page, D.L., Koschan, A.F. and Abidi, M.A. (2008), “Towards understanding what makes 3D objects appear simple or complex”, in 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, IEEE, pp. 1–8. http://doi.org/10.1109/CVPRW.2008.4562975.CrossRefGoogle Scholar
Tang, Y. and Gu, H. (2010), “CAD Model's Simplification and Conversion for Virtual Reality”, in Jiang, Y. (Ed.), 2010 Third International Conference on Information and Computing, IEEE, Piscataway, NJ, pp. 265–268. http://doi.org/10.1109/ICIC.2010.338.Google Scholar
Yildiz, Z.C., Oztireli, A.C. and Capin, T. (2020), “A machine learning framework for full-reference 3D shape quality assessment”, The Visual Computer, Vol. 36 No. 1, pp. 127–139. http://doi.org/10.1007/s00371-018-1592-9.CrossRefGoogle Scholar