{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T13:55:14Z","timestamp":1772114114799,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,2,4]],"date-time":"2024-02-04T00:00:00Z","timestamp":1707004800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Three-dimensional models, reconstructed from real-life objects, are extensively used in virtual and mixed reality technologies. In this paper we propose an approach to 3D model reconstruction via inverse procedural modeling and describe two variants of this approach. The first option is to fit a set of input parameters using a genetic algorithm. The second option allows us to significantly improve precision by using gradients within the memetic algorithm, differentiable rendering, and differentiable procedural generators. We demonstrate the results of our work on different models, including trees, which are complex objects that most existing methods cannot reconstruct. In our work, we see two main contributions. First, we propose a method to join differentiable rendering and inverse procedural modeling. This gives us the ability to reconstruct 3D models more accurately than existing approaches when few input images are available, even for a single image. Second, we combine both differentiable and non-differentiable procedural generators into a single framework that allows us to apply inverse procedural modeling to fairly complex generators. We show that both variants of our approach can be useful: the differentiable one is more precise but puts limitations on the procedural generator, while the one based on genetic algorithms can be used with any existing generator. The proposed approach uses information about the symmetry and structure of the object to achieve high-quality reconstruction from a single image.<\/jats:p>","DOI":"10.3390\/sym16020184","type":"journal-article","created":{"date-parts":[[2024,2,5]],"date-time":"2024-02-05T05:22:44Z","timestamp":1707110564000},"page":"184","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Single-View 3D Reconstruction via Differentiable Rendering and Inverse Procedural Modeling"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3802-1774","authenticated-orcid":false,"given":"Albert","family":"Garifullin","sequence":"first","affiliation":[{"name":"Laboratory of Computer Graphics and Multimedia, Faculty of Computational Mathematics and Cybernetics, Moscow State University, 119991 Moscow, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2677-881X","authenticated-orcid":false,"given":"Nikolay","family":"Maiorov","sequence":"additional","affiliation":[{"name":"Laboratory of Computer Graphics and Multimedia, Faculty of Computational Mathematics and Cybernetics, Moscow State University, 119991 Moscow, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8829-9884","authenticated-orcid":false,"given":"Vladimir","family":"Frolov","sequence":"additional","affiliation":[{"name":"Laboratory of Computer Graphics and Multimedia, Faculty of Computational Mathematics and Cybernetics, Moscow State University, 119991 Moscow, Russia"},{"name":"Department of Computer Graphics and Computational Optics, Keldysh Institute of Applied Mathematics RAS, 125047 Moscow, Russia"},{"name":"Institute of Artificial Intelligence of Moscow State University (IAI MSU), 119192 Moscow, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1252-8294","authenticated-orcid":false,"given":"Alexey","family":"Voloboy","sequence":"additional","affiliation":[{"name":"Department of Computer Graphics and Computational Optics, Keldysh Institute of Applied Mathematics RAS, 125047 Moscow, Russia"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1111\/cgf.14505","article-title":"Neural fields in visual computing and beyond","volume":"41","author":"Xie","year":"2022","journal-title":"Comput. Graph. Forum"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Pontes, J.K., Kong, C., Sridharan, S., Lucey, S., Eriksson, A., and Fookes, C. (2018, January 2\u20136). Image2mesh: A learning framework for single image 3d reconstruction. Proceedings of the Computer Vision\u2014ACCV 2018: 14th Asian Conference on Computer Vision, Perth, WA, Australia.","DOI":"10.1007\/978-3-030-20887-5_23"},{"key":"ref_3","unstructured":"Yang, X., Lin, G., and Zhou, L. (2022). ZeroMesh: Zero-shot Single-view 3D Mesh Reconstruction. arXiv."},{"key":"ref_4","unstructured":"Rakotosaona, M.J., Manhardt, F., Arroyo, D.M., Niemeyer, M., Kundu, A., and Tombari, F. (2023). NeRFMeshing: Distilling Neural Radiance Fields into Geometrically-Accurate 3D Meshes. arXiv."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Tang, J., Zhou, H., Chen, X., Hu, T., Ding, E., Wang, J., and Zeng, G. (2023). Delicate Textured Mesh Recovery from NeRF via Adaptive Surface Refinement. arXiv.","DOI":"10.1109\/ICCV51070.2023.01626"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1111\/cgf.14344","article-title":"Unified Shape and SVBRDF Recovery using Differentiable Monte Carlo Rendering","volume":"40","author":"Luan","year":"2021","journal-title":"Comput. Graph. Forum"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Park, J.J., Florence, P., Straub, J., Newcombe, R., and Lovegrove, S. (2019, January 16\u201320). Deepsdf: Learning continuous signed distance functions for shape representation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00025"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Garifullin, A., Shcherbakov, A., and Frolov, V. (2022, January 17\u201320). Fitting Parameters for Procedural Plant Generation. Proceedings of the WSCG 2022: 30 International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, Pilsen, Czech Republic.","DOI":"10.24132\/CSRN.3201.35"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Garifullin, A., Maiorov, N., and Frolov, V. (2023, January 14\u201315). Differentiable Procedural Models for Single-view 3D Mesh Reconstruction. Proceedings of the Computer Graphics and Visual Computing (CGVC), Aberystwyth University, Wales, UK.","DOI":"10.20948\/graphicon-2023-14-24"},{"key":"ref_10","unstructured":"Schonberger, J.L., and Frahm, J.M. (July, January 26). Structure-from-motion revisited. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1109\/TRO.2016.2624754","article-title":"Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age","volume":"32","author":"Cadena","year":"2016","journal-title":"IEEE Trans. Robot."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, N., Zhang, Y., Li, Z., Fu, Y., Liu, W., and Jiang, Y.G. (2018, January 8\u201314). Pixel2mesh: Generating 3d mesh models from single rgb images. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01252-6_4"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Nie, Y., Han, X., Guo, S., Zheng, Y., Chang, J., and Zhang, J.J. (2020, January 14\u201319). Total3dunderstanding: Joint layout, object pose and mesh reconstruction for indoor scenes from a single image. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Virtual.","DOI":"10.1109\/CVPR42600.2020.00013"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ye, Y., Tulsiani, S., and Gupta, A. (2021, January 19\u201325). Shelf-supervised mesh prediction in the wild. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Virtual.","DOI":"10.1109\/CVPR46437.2021.00873"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Choy, C.B., Xu, D., Gwak, J., Chen, K., and Savarese, S. (2016, January 11\u201314). 3d-r2n2: A unified approach for single and multi-view 3d object reconstruction. Proceedings of the Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46484-8_38"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Popov, S., Bauszat, P., and Ferrari, V. (2020, January 23\u201328). Corenet: Coherent 3d scene reconstruction from a single rgb image. Proceedings of the Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK.","DOI":"10.1007\/978-3-030-58536-5_22"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Fan, H., Su, H., and Guibas, L.J. (2017, January 22\u201325). A point set generation network for 3d object reconstruction from a single image. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.264"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chen, C., Han, Z., Liu, Y.S., and Zwicker, M. (2021, January 11\u201319). Unsupervised learning of fine structure generation for 3d point clouds by 2d projections matching. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Virtual.","DOI":"10.1109\/ICCV48922.2021.01224"},{"key":"ref_19","unstructured":"Chen, W., Ling, H., Gao, J., Smith, E., Lehtinen, J., Jacobson, A., and Fidler, S. (2019). Advances in Neural Information Processing Systems, Springer."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tatarchenko, M., Richter, S.R., Ranftl, R., Li, Z., Koltun, V., and Brox, T. (2019, January 16\u201320). What do single-view 3d reconstruction networks learn?. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00352"},{"key":"ref_21","unstructured":"Zhang, X., Zhang, Z., Zhang, C., Tenenbaum, J., Freeman, B., and Wu, J. (2018). Advances in Neural Information Processing Systems, Springer."},{"key":"ref_22","unstructured":"Zou, Z.X., Yu, Z., Guo, Y.C., Li, Y., Liang, D., Cao, Y.P., and Zhang, S.H. (2023). Triplane Meets Gaussian Splatting: Fast and Generalizable Single-View 3D Reconstruction with Transformers. arXiv."},{"key":"ref_23","first-page":"194","article-title":"Modular primitives for high-performance differentiable rendering","volume":"39","author":"Laine","year":"2020","journal-title":"ACM Trans. Graph. TOG"},{"key":"ref_24","first-page":"76","article-title":"Path-space differentiable rendering of participating media","volume":"40","author":"Zhang","year":"2021","journal-title":"ACM Trans. Graph. TOG"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Deng, X., Luan, F., Walter, B., Bala, K., and Marschner, S. (2022, January 8\u201311). Reconstructing translucent objects using differentiable rendering. Proceedings of the ACM SIGGRAPH 2022 Conference, Vancouver, BC, Canada.","DOI":"10.1145\/3528233.3530714"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Bangaru, S.P., Gharbi, M., Luan, F., Li, T.M., Sunkavalli, K., Hasan, M., Bi, S., Xu, Z., Bernstein, G., and Durand, F. (2022, January 6\u20139). Differentiable rendering of neural SDFs through reparameterization. Proceedings of the SIGGRAPH Asia 2022 Conference, Daegu, Republic of Korea.","DOI":"10.1145\/3550469.3555397"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wickramasinghe, U., Fua, P., and Knott, G. (2021, January 19\u201325). Deep Active Surface Models. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Virtual.","DOI":"10.1109\/CVPR46437.2021.01148"},{"key":"ref_28","first-page":"73","article-title":"Fast quasi-harmonic weights for geometric data interpolation","volume":"40","author":"Wang","year":"2021","journal-title":"ACM Trans. Graph. TOG"},{"key":"ref_29","first-page":"248","article-title":"Large steps in inverse rendering of geometry","volume":"40","author":"Nicolet","year":"2021","journal-title":"ACM Trans. Graph. TOG"},{"key":"ref_30","first-page":"125","article-title":"Differentiable signed distance function rendering","volume":"41","author":"Vicini","year":"2022","journal-title":"ACM Trans. Graph. TOG"},{"key":"ref_31","unstructured":"Oswald, M.R. (2015). Convex Variational Methods for Single-View and Space-Time Multi-View Reconstruction. [Ph.D. Thesis, Technische Universit\u00e4t M\u00fcnchen]. Available online: https:\/\/mediatum.ub.tum.de\/doc\/1232437\/928830.pdf."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Lombardi, S., Simon, T., Saragih, J., Schwartz, G., Lehrmann, A., and Sheikh, Y. (2019). Neural volumes: Learning dynamic renderable volumes from images. arXiv.","DOI":"10.1145\/3306346.3323020"},{"key":"ref_33","first-page":"136","article-title":"A non-exponential transmittance model for volumetric scene representations","volume":"40","author":"Vicini","year":"2021","journal-title":"ACM Trans. Graph. TOG"},{"key":"ref_34","first-page":"230","article-title":"Differentiable surface splatting for point-based geometry processing","volume":"38","author":"Yifan","year":"2019","journal-title":"ACM Trans. Graph. TOG"},{"key":"ref_35","first-page":"99","article-title":"ADOP: Approximate differentiable one-pixel point rendering","volume":"41","author":"Franke","year":"2022","journal-title":"ACM Trans. Graph. TOG"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1145\/3503250","article-title":"Nerf: Representing scenes as neural radiance fields for view synthesis","volume":"65","author":"Mildenhall","year":"2021","journal-title":"Commun. ACM"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Fridovich-Keil, S., Yu, A., Tancik, M., Chen, Q., Recht, B., and Kanazawa, A. (2022, January 19\u201324). Plenoxels: Radiance fields without neural networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00542"},{"key":"ref_38","first-page":"102","article-title":"Instant neural graphics primitives with a multiresolution hash encoding","volume":"41","author":"Evans","year":"2022","journal-title":"ACM Trans. Graph. TOG"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2422956.2422957","article-title":"Procedural content generation for games: A survey","volume":"9","author":"Hendrikx","year":"2013","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl. TOMM"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Freiknecht, J., and Effelsberg, W. (2017). A survey on the procedural generation of virtual worlds. Multimodal Technol. Interact., 1.","DOI":"10.3390\/mti1040027"},{"key":"ref_41","unstructured":"SpeedTree (2023, November 11). Available online: http:\/\/www.speedtree.com."},{"key":"ref_42","unstructured":"Prusinkiewicz, P., and Lindenmayer, A. (2012). The Algorithmic Beauty of Plants, Springer."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1111\/cgf.13263","article-title":"Tree growth modelling constrained by growth equations","volume":"37","author":"Yi","year":"2018","journal-title":"Comput. Graph. Forum"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1111\/cgf.12282","article-title":"Inverse procedural modelling of trees","volume":"33","author":"Stava","year":"2014","journal-title":"Comput. Graph. Forum"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Demir, I., Aliaga, D.G., and Benes, B. (2016, January 25\u201328). Proceduralization for editing 3d architectural models. Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA.","DOI":"10.1109\/3DV.2016.28"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Aliaga, D.G., Demir, \u0130., Benes, B., and Wand, M. (2018, January 24\u201328). Inverse procedural modeling of 3d models for virtual worlds. Proceedings of the ACM SIGGRAPH 2016 Courses, Anaheim, CA, USA.","DOI":"10.1145\/2897826.2927323"},{"key":"ref_47","first-page":"155","article-title":"Inverse procedural modeling of branching structures by inferring L-systems","volume":"39","author":"Guo","year":"2020","journal-title":"ACM Trans. Graph. TOG"},{"key":"ref_48","first-page":"18","article-title":"An inverse procedural modeling pipeline for SVBRDF maps","volume":"41","author":"Hu","year":"2022","journal-title":"ACM Trans. Graph. TOG"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Wu, F., Yan, D.M., Dong, W., Zhang, X., and Wonka, P. (2013). Inverse procedural modeling of facade layouts. arXiv.","DOI":"10.1145\/2601097.2601162"},{"key":"ref_50","first-page":"51","article-title":"Fitting procedural yarn models for realistic cloth rendering","volume":"35","author":"Zhao","year":"2016","journal-title":"ACM Trans. Graph. TOG"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Trunz, E., Klein, J., M\u00fcller, J., Bode, L., Sarlette, R., Weinmann, M., and Klein, R. (2023). Neural inverse procedural modeling of knitting yarns from images. arXiv.","DOI":"10.1016\/j.cag.2023.12.013"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/cgf.14475","article-title":"Automatic differentiable procedural modeling","volume":"41","author":"Gaillard","year":"2022","journal-title":"Comput. Graph. Forum"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Zeng, J., Zhang, Y., Zhan, S., and Liu, C. (2006, January 18\u201320). Reconstructing symmetric curved surfaces from a single image and its application. Proceedings of the Interactive Technologies and Sociotechnical Systems: 12th International Conference, VSMM 2006, Xi\u2019an, China.","DOI":"10.1007\/11890881_23"},{"key":"ref_54","first-page":"4117409","article-title":"Single-View 3d Reconstruction of Surface of Revolution","volume":"12","author":"Hosseini","year":"2022","journal-title":"SSRN"},{"key":"ref_55","first-page":"102859","article-title":"3D building reconstruction from single street view images using deep learning","volume":"112","author":"Pang","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_56","unstructured":"Venkat, A., Jinka, S.S., and Sharma, A. (2018). Deep textured 3d reconstruction of human bodies. arXiv."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"103628","DOI":"10.1016\/j.dsp.2022.103628","article-title":"A review of 3D human body pose estimation and mesh recovery","volume":"128","author":"Huang","year":"2022","journal-title":"Digit. Signal Process."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1145\/3592433","article-title":"3D Gaussian Splatting for Real-Time Radiance Field Rendering","volume":"42","author":"Kerbl","year":"2023","journal-title":"ACM Trans. Graph."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Rakotosaona, M.J., Manhardt, F., Arroyo, D.M., Niemeyer, M., Kundu, A., and Tombari, F. (2023, January 12\u201315). NeRFMeshing: Distilling Neural Radiance Fields into Geometrically-Accurate 3D Meshes. Proceedings of the International Conference on 3D Vision (3DV), Prague, Czech Republic.","DOI":"10.1109\/3DV62453.2024.00093"},{"key":"ref_60","unstructured":"Wenzel, J., Speierer, S., Roussel, N., Nimier-David, M., Vicini, D., Zeltner, T., Nicolet, B., Crespo, M., Leroy, V., and Zhang, Z. (2023, November 11). Mitsuba 3 Renderer. Available online: https:\/\/mitsuba-renderer.org."},{"key":"ref_61","first-page":"10","article-title":"CppAD: A package for C++ algorithmic differentiation","volume":"57","author":"Bell","year":"2012","journal-title":"Comput. Infrastruct. Oper. Res."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Mitchell, M. (1998). An Introduction to Genetic Algorithms, MIT Press.","DOI":"10.7551\/mitpress\/3927.001.0001"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.swevo.2011.11.003","article-title":"Memetic algorithms and memetic computing optimization: A literature review","volume":"2","author":"Neri","year":"2012","journal-title":"Swarm Evol. Comput."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1111\/cgf.13106","article-title":"Interactive modeling and authoring of climbing plants","volume":"36","author":"Benes","year":"2017","journal-title":"Comput. Graph. Forum"},{"key":"ref_65","unstructured":"Yi, L., Li, H., Guo, J., Deussen, O., and Zhang, X. (2015, January 7\u20139). Light-Guided Tree Modeling of Diverse Biomorphs. Proceedings of the 23rd Pacific Conference on Computer Graphics and Applications \u201cPacific Graphics 2015\u201d, Beijing, China."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Weber, J., and Penn, J. (1995, January 6\u201311). Creation and rendering of realistic trees. Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques, Los Angeles, CA, USA.","DOI":"10.1145\/218380.218427"},{"key":"ref_67","first-page":"231","article-title":"Learning to reconstruct botanical trees from single images","volume":"40","author":"Li","year":"2021","journal-title":"ACM Trans. Graph. TOG"},{"key":"ref_68","first-page":"222","article-title":"Differentiable monte carlo ray tracing through edge sampling","volume":"37","author":"Li","year":"2018","journal-title":"ACM Trans. Graph. TOG"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/16\/2\/184\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:54:47Z","timestamp":1760104487000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/16\/2\/184"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,4]]},"references-count":68,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["sym16020184"],"URL":"https:\/\/doi.org\/10.3390\/sym16020184","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,4]]}}}