IDOL Reconstruction Using Deep Learning and Additive Manufacturing: A Framework for Cultural Heritage Restoration

Tejaswini Bhosale; Aditya Pathak; Deepika Nikam; Mayur Sawant

doi:10.2991/978-94-6239-813-9_6

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IDOL Reconstruction Using Deep Learning and Additive Manufacturing: A Framework for Cultural Heritage Restoration

Authors

Tejaswini Bhosale¹^{, *}, Aditya Pathak¹, Deepika Nikam², Mayur Sawant¹

¹MIT Art Design & Technology University, Pune, India

²School of Computing, Engineering, and Intelligent Systems, Ulster University, Magee Campus, Northland Road, Derry/Londonderry, BT48 7JL, Northern Ireland, UK

^*Corresponding author. Email: tejaswini.bhosale@mituniversity.edu.in

Corresponding Author

Tejaswini Bhosale

Available Online 29 October 2026.

DOI: 10.2991/978-94-6239-813-9_6 How to use a DOI?
Keywords: Idol Reconstruction; Cultural Heritage; Fused Deposition Modelling; 3d Printing; Deep Learning; AI
Abstract: Often times, cultural heritage artifacts and idols are damaged by aging, environmental conditions, accidents and natural disasters. Traditional methods for the restoration are time consuming and need skilled artisans. The proposed approach an Idol reconstruction framework based on an AI-assisted additive manufacturing. A deep learning model is used to detect and reconstruct the damaged idol regions from the damaged images. The reconstructed geometry is turned into a 3D model and printed in fused deposition modelling (FDM) 3D printing technology. This proposed approach combines computer vision, deep learning, and additive manufacturing technologies to restore broken parts of the idol precisely while maintaining the artistic and cultural value.
Copyright: © 2026 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the Global Symposium on Adaptive Manufacturing (GSAM 2026)
Series: Advances in Engineering Research
Publication Date: 29 October 2026
ISBN: 978-94-6239-813-9
ISSN: 2352-5401
DOI: 10.2991/978-94-6239-813-9_6 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Tejaswini Bhosale
AU  - Aditya Pathak
AU  - Deepika Nikam
AU  - Mayur Sawant
PY  - 2026
DA  - 2026/10/29
TI  - IDOL Reconstruction Using Deep Learning and Additive Manufacturing: A Framework for Cultural Heritage Restoration
BT  - Proceedings of the  Global Symposium on Adaptive Manufacturing (GSAM 2026)
PB  - Atlantis Press
SP  - 16
EP  - 18
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-813-9_6
DO  - 10.2991/978-94-6239-813-9_6
ID  - Bhosale2026
ER  -

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