3D PRINTING FOR PHOTOGRAPHIC RELIEF SCULPTURES
DOI:
https://doi.org/10.29121/shodhkosh.v6.i5s.2025.6887Keywords:
3D Printing, Semantic Weighting, Additive Manufacturing, Digital Art, Cultural Heritage Maintenance, Physical VisualizingAbstract [English]
The paper introduces a semiotic computational methodology of generating 3D-printer photographic relief sculptures that includes depth estimation by artificial intelligence, semantic weight, and adaptive fabrication algorithms, the transformation of the two-dimensional photographic data into the three-dimensional object itself. In such a method, the discrepancy between the computational vision and materialization is bridged with the help of a hybrid workflow which proposes image preprocessing, semantic features extraction, and non-linear depth compression. The outcome of such synthesis is a better surface fidelity, perceptual realism and aesthetic consistency which is a huge advancement over the old methods of mapping grayscale to depth. The experimental verification indicates that the model is capable of balancing the technical and expressivity with the artistic means, which can be verified by the fact that the geometric accuracy has been increased by 12% and that the interpretive quality rated by curators has been increased. The framework is highly inclusive and cultural continuity besides being calculative and creative and it offers viable solutions to heritage conservation, access to museums and manufacture of digital art as well. All these contributions enable establishing a platform to a new type of AI-assisted artistic work that recreates how visual images might be viewed as a sculptural and sensory object.
References
Badar, F., Dean, L. T., Loy, J., Redmond, M., Vandi, L.-J., and Novak, J. I. (2023). Preliminary Color Characterization of HP Multi Jet Fusion Additive Manufacturing With Different Orientations and Surface Finish. Rapid Prototyping Journal, 29, 582–593. DOI: https://doi.org/10.1108/RPJ-04-2022-0136
Ballarin, M., Balletti, C., and Vernier, P. (2018). Replicas in Cultural Heritage: 3D Printing and the Museum Experience. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 55–62. https://doi.org/10.5194/isprs-archives-XLII-2-55-2018 DOI: https://doi.org/10.5194/isprs-archives-XLII-2-55-2018
Balletti, C., and Ballarin, M. (2019). An Application of Integrated 3D Technologies for Replicas in Cultural Heritage. ISPRS International Journal of Geo-Information, 8, 285. https://doi.org/10.3390/ijgi8070285 DOI: https://doi.org/10.3390/ijgi8060285
Cader, M., and Kiński, W. (2020). Effect of Changing the Parameters of the Multi Jet Fusion (MJF) Process on the Spatial Objects Produced. Problems of Mechatronics: Armament, Aviation, Safety Engineering, 11, 61–72. DOI: https://doi.org/10.5604/01.3001.0014.5644
Cignoni, P., and Rocchini, C. (2007). Application of High-Resolution Scanning Systems for Virtual Moulds and Replicas of Sculptural Works. In Proceedings of the XXI CIPA International Symposium: Anticipating the Future of the Cultural Past, Athens, Greece.
Hai, V. N. T., Phu, S. N., Essomba, T., and Lai, J.-Y. (2022). Development of a Multicolor 3D Printer Using a Novel Filament Shifting Mechanism. Inventions, 7, 34. https://doi.org/10.3390/inventions7020034 DOI: https://doi.org/10.3390/inventions7020034
Izonin, I., Tkachenko, R., Gregus, M., Ryvak, L., Kulyk, V., and Chopyak, V. (2021). Hybrid Classifier via PNN-Based Dimensionality Reduction Approach for Biomedical Engineering Task. Procedia Computer Science, 191, 230–237. https://doi.org/10.1016/j.procs.2021.07.029 DOI: https://doi.org/10.1016/j.procs.2021.07.029
Jo, B. W., and Song, C. S. (2021). Thermoplastics and Photopolymer Desktop 3D Printing System Selection Criteria Based on Technical Specifications and Performances for Instructional Applications. Technologies, 9, 91. Mahmood, T., Akram, H., Chen, H., and Chen, S. (2022). On the Evolution of Additive Manufacturing (3D/4D Printing) Technologies: Materials, Applications, and Challenges. Polymers, 14, 4698. https://doi.org/10.3390/polym14214698 DOI: https://doi.org/10.3390/technologies9040091
Kantaros, T., Ganetsos, T., and Piromalis, D. (2023). 3D and 4D Printing as Integrated Manufacturing Methods of Industry 4.0. American Journal of Engineering and Applied Sciences, 16, 12–22. DOI: https://doi.org/10.3844/ajeassp.2023.12.22
Kantaros, T., Ganetsos, T., and Piromalis, D. (2023). 4D Printing: Technology Overview and Smart Materials Utilized. Journal of Mechatronics and Robotics, 7, 1–14. DOI: https://doi.org/10.3844/jmrsp.2023.1.14
Kshirsagar, R. M., Kherde, S. M., and Dharmadhikari, S. R. (2024). Investigation of 3D Printed Lithophane Quality Improvement on an FDM Printer. In Proceedings of the 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), 1488–1494, Erode, India. DOI: https://doi.org/10.1109/ICSSAS64001.2024.10760618
Laureto, J. J., and Pearce, J. M. (2017). Open Source Multi-Head 3D Printer for Polymer–Metal Composite Component Manufacturing. Technologies, 5, 36. https://doi.org/10.3390/technologies5020036 DOI: https://doi.org/10.3390/technologies5020036
Petsiuk, A., Bloch, B., Debora, M., and Pearce, J. M. (2024). Tool Change Reduction for Multicolor Fused Filament Fabrication Through Interlayer Tool Clustering Implemented in PrusaSlicer. Rapid Prototyping Journal, 30, 1592–1609. DOI: https://doi.org/10.1108/RPJ-01-2024-0050
Tkachenko, R., Duriagina, Z., Lemishka, I., Izonin, I., and Trostianchyn, A. (2018). Development of Machine Learning Method of Titanium Alloy Properties Identification in Additive Technologies. Eastern-European Journal of Enterprise Technologies, 3, 23–31. DOI: https://doi.org/10.15587/1729-4061.2018.134319
Tucci, G., and Bonora, V. (2011). From Real to “Real”: A Review of Geomatic and Rapid Prototyping Techniques for Solid Modelling in Cultural Heritage Field. ISPRS International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37, 575–582. DOI: https://doi.org/10.5194/isprsarchives-XXXVIII-5-W16-575-2011
Xia, L., and Yan, R. (2025). A Fast Slicing Method for Colored Models Based on Colored Triangular Prism and OpenGL. Micromachines, 16, 199. https://doi.org/10.3390/micromachines16020199 DOI: https://doi.org/10.3390/mi16020199
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Copyright (c) 2025 Mr. Jenifer Patel, Shikha Gupta, Senthil Jayapalan, Sachin Pratap Singh, Anoop Dev, Aditi Ashish Deokar
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