3D visualization of damaged statues using Gaussian splatting and web interface integration
Source abstract
Abstract
Digital heritage conservation relies significantly on advanced digital documentation techniques like photogrammetry and laser scanning. These non-invasive technologies enable precise documentation, analysis, and preservation of cultural heritage. This research presents a framework for visualizing the restoration of statues through five stages: (1) Dataset Collection, (2) Applying Several Techniques for 3D Reconstruction, (3) Selecting the Optimal Method for 3D Reconstruction, (4) Visualizing the Statue, and (5) Deploying the Statue on a Web-Based Platform. Three methods for 3D Reconstruction were compared to determine the most effective. The evaluation compared photogrammetry, Gaussian Splatting, and an integrated laser scanning–photogrammetry method. Gaussian Splatting achieves competitive geometric accuracy according to the measured Hausdorff Distances. Gaussian Splatting is considered the most suitable for rapid and resource-efficient visualization, particularly when minimal computational power and straightforward web deployment are required. The framework is implemented on an Egyptian statue of a Ptolemaic queen to illustrate its use.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: 3D Surveying and Cultural Heritage · Building materials and conservation · Image Processing and 3D Reconstruction
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (70)
Show source-provided references
1. Mohamed, B. & Marzouk, M. Bibliometric analysis and visualisation of heritage buildings preservation. Herit. Sci. 11, 101 (2023).10.1186/s40494-023-00947-y ↗
2. Xiao, W. et al. Geoinformatics for the conservation and promotion of cultural heritage in support of the UN Sustainable Development Goals. ISPRS J. Photogramm. Remote Sens. 142, 389–406 (2018).10.1016/j.isprsjprs.2018.01.001 ↗
3. Rosetti, I. et al. Heritage and sustainability: regulating participation. Sustainability 14, 1674 (2022).10.3390/su14031674 ↗
4. Gražulevičiūtė, I. Cultural heritage in the context of sustainable development. Environ. Res., Eng. Manag. 37, 74–79 (2006).
5. Siliutina, I. et al. Cultural preservation and digital heritage: challenges and opportunities. Amazon. Investig. 13, 262–273 (2024).10.34069/AI/2024.75.03.22 ↗
6. Banda, L. O. et al. Preserving cultural heritage: a community-centric approach to safeguarding the Khulubvi Traditional Temple Malawi. Heliyon 10, e37610 (2024).10.1016/j.heliyon.2024.e37610 ↗
7. Frigerio, A. The Fate of Statues of Stalin in post-Soviet Countries: Some critical reflections on the management of contested cultural heritage. Herit. Soc. 12, 136–150 (2019).10.1080/2159032X.2021.1909416 ↗
8. Tong, Y. et al. Digital technology virtual restoration of the colours and textures of polychrome Bodhidharma statue from the Lingyan Temple, Shandong, China. Herit. Sci. 11, 12 (2023).10.1186/s40494-023-00858-y ↗
9. Stylianidis, E. Photogrammetric Survey for the Recording and Documentation of Historic Buildings. Springer: Cham, Switzerland, (2020).10.1007/978-3-030-47310-5 ↗
10. Tobiasz, A. et al. Review of methods for documentation, management, and sustainability of cultural heritage. case study: Museum of King Jan III’s Palace at Wilanów. Sustainability 11, 7046 (2019).10.3390/su11247046 ↗
11. Yue, H. et al. Deep learning applications for point clouds in the construction industry. Autom. Constr. 168, 105769 (2024).10.1016/j.autcon.2024.105769 ↗
12. Marzouk, M. Using 3D laser scanning to analyze heritage structures: The case study of Egyptian palace. J. Civ. Eng. Manag. 26, 53–65 (2020).10.3846/jcem.2020.11520 ↗
13. El-Maraghy, M. et al. Predicting energy consumption of mosque buildings during the operation stage using deep learning approach. Energy Build 303, 113829 (2024).10.1016/j.enbuild.2023.113829 ↗
14. Willkens, D. S. et al. A Case Study of Integrating Terrestrial Laser Scanning (TLS) and Building Information Modeling (BIM) in Heritage Bridge Documentation: The Edmund Pettus Bridge. Buildings 14, 1940 (2024).10.3390/buildings14071940 ↗
15. Kong, X. & Hucks, R. G. Preserving our heritage: A photogrammetry-based digital twin framework for monitoring deteriorations of historic structures. Autom. Constr. 152, 104928 (2023).10.1016/j.autcon.2023.104928 ↗
16. Baltsavias, E. P. A comparison between photogrammetry and laser scanning. 54, 83–94 (1999).10.1016/S0924-2716(99)00014-3 ↗
17. Yilmaz, H. M. et al. Importance of digital close-range photogrammetry in documentation of cultural heritage. J. Cult. Herit. 8, 428–433 (2007).10.1016/j.culher.2007.07.004 ↗
18. Grün, A. et al. Photogrammetric reconstruction of the great Buddha of Bamiyan, Afghanistan. Photogramm. Rec. 19, 177–199 (2004).10.1111/j.0031-868X.2004.00278.x ↗
19. Napolitano, R. K. et al. Virtual tours and informational modeling for conservation of cultural heritage sites. J. Cult. Herit. 29, 123–129 (2018).10.1016/j.culher.2017.08.007 ↗
20. Dostal, C. & Yamafune, K. Photogrammetric texture mapping: A method for increasing the Fidelity of 3D models of cultural heritage materials. J. Archaeol. Sci. Rep. 18, 430–436 (2018).
21. Alshawabkeh, Y. & Baik, A. Integration of photogrammetry and laser scanning for enhancing scan-to-HBIM modeling of Al Ula heritage site. Herit. Sci. 11, 147 (2023).10.1186/s40494-023-00997-2 ↗
22. Mikita, T. et al. Comparison of different remote sensing methods for 3d modeling of small rock outcrops. Sensors 20, 1663 (2020).10.3390/s20061663 ↗
23. Kudela, P. et al. Integration of photogrammetry within laser scanning approach. In 2020 43rd International Convention on Information, Communication and Electronic Technology, MIPRO 2020 - Proceedings, Institute of Electrical and Electronics Engineers Inc. p. 1691–11694 (2020).10.23919/MIPRO48935.2020.9245297 ↗
24. Zachos, A. & Anagnostopoulos, C.-N. Using Terrestrial Laser Scanning, Unmanned Aerial Vehicles and Mixed Reality Methodologies for Digital Survey, 3D Modelling and Historical Recreation of Religious Heritage Monuments. arXiv preprint arXiv:2401.01380 (2023).10.1145/3679021 ↗
25. Rönnholm, P. et al. Integration Of Laser Scanning and Photogrammetry. International Archives of Photogrammetry, Remote Sensing and Spatial. Inf. Sci. 36, 355–362 (2007).
26. Zhang, Z. et al. UnrollingNet: An attention-based deep learning approach for the segmentation of large-scale point clouds of tunnels. Autom. Constr. 142, 104456 (2022).10.1016/j.autcon.2022.104456 ↗
27. Kim, H. & Kim, C. Deep-learning-based classification of point clouds for bridge inspection. Remote Sens. 12, 3757 (2020).10.3390/rs12223757 ↗
28. Wang, N. et al. An automatic defect classification and segmentation method on three-dimensional point clouds for sewer pipes. Tunn. Undergr. Space Technol. 143, 105480 (2024).10.1016/j.tust.2023.105480 ↗
29. Maini, R. & Aggarwal, H. A Comprehensive Review of Image Enhancement Techniques. arXiv preprint arXiv:1003.4053 (2010).
30. Silva, C. & Oliveira, L. Artificial intelligence at the interface between cultural heritage and photography: A systematic literature review. Heritage 7, 3799–3820 (2024).10.3390/heritage7070180 ↗
31. Fernández Rodríguez, R. et al. Potential of mobile application based on structure from motion (SfM) photogrammetry to monitor slope fast erosion by runoff water. Catena 216, 106359 (2022).10.1016/j.catena.2022.106359 ↗
32. Zhang, C. et al. Assessing through-water structure-from-motion photogrammetry in gravel-bed rivers under controlled conditions. Remote Sens 14, 5351 (2022).10.3390/rs14215351 ↗
33. Voumard, J. et al. Pros and cons of structure for motion embarked on a vehicle to survey slopes along transportation lines using 3D georeferenced and coloured point clouds. Remote Sens. 10, 1732 (2018).10.3390/rs10111732 ↗
34. Cai, Z. et al. 3D Reconstruction of Buildings Based on 3D Gaussian Splatting. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS Arch. 48, 37–43 (2024).
35. Croce, V. et al. Comparative assessment of neural radiance fields and photogrammetry in digital heritage: impact of varying image conditions on 3D reconstruction. Remote Sens 16, 301 (2024).10.3390/rs16020301 ↗
36. Chen, G. & Wang, W. A Survey on 3D Gaussian Splatting. arXiv preprint arXiv:2401.03890 (2024).
37. Guo, Z. et al. GP-GS: Gaussian Processes for Enhanced Gaussian Splatting. arXiv preprint arXiv:2502.02283 (2025).
38. Schönberger, J. L. & Frahm, J.-M. Structure-from-Motion Revisited. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4104-4113 (2016).10.1109/CVPR.2016.445 ↗
39. Bao, Y. et al. 3D Gaussian Splatting: Survey, Technologies. Chall., Opportun. 5, 6832–6852 (2025).
40. Wang, Y. et al. FreeSplat: Generalizable 3D Gaussian Splatting Towards Free-View Synthesis of Indoor Scenes. 37, pp. 107326-107349 (2024).10.52202/079017-3409 ↗
41. Zhu, S. et al. 3D Gaussian Splatting in Robotics: A Survey. arXiv preprint arXiv:2410.12262 (2024).
42. Kerbl, B. et al. 3D Gaussian Splatting for Real-Time Radiance Field Rendering. ACM Trans. Graph. 42, 139–1 (2023).10.1145/3592433 ↗
43. Fei, B. et al. 3D Gaussian as a New Era: A Survey. IEEE Trans. Vis. Comput. Graph. 31, 4429–4449 (2025).10.1109/TVCG.2024.3397828 ↗
44. Jiang, Z. et al. 3D points splatting for real-time dynamic hand reconstruction. Pattern Recognit. 162, 111426 (2025).10.1016/j.patcog.2025.111426 ↗
45. Dahaghin, M. et al. Gaussian Heritage: 3D Digitization of Cultural Heritage with Integrated Object Segmentation. arXiv preprint arXiv:2409.19039 (2024).
46. VOSviewer - Visualizing scientific landscapes. https://www.vosviewer.com/ (2025).
47. Cheng, L. et al. Integration of LiDAR data and optical multi-view images for 3D Reconstruction of building roofs. Opt. Lasers Eng. 51, 493–502 (2013).10.1016/j.optlaseng.2012.10.010 ↗
48. AliceVision | Photogrammetric Computer Vision Framework. https://alicevision.org/ (2025).
49. Griwodz, C. et al. AliceVision Meshroom: An open-source 3D reconstruction pipeline. In Proceedings of the 12th ACM multimedia systems conference, pp. 241-247 (2021).10.1145/3458305.3478443 ↗
50. Enesi, I. & Kuqi, A. Evaluation of the 3D reconstruction performance of objects in Meshroom: A case study. Athens J. Τechnol. Eng. 10, 49–70 (2023).10.30958/ajte.10-1-3 ↗
51. Alshawabkeh, Y. & Haala, N. Integration of laser scanning and photogrammetry for heritage documentation. Ph.D. Thesis, Institut für Photogrammetrie, Universität Stuttgart, Stutgart, Germany. (2006).
52. RealityCapture - 3D Models from Photos and/or Laser Scans. https://www.capturingreality.com/ (2025).
53. Explore-Lasers-Images - Capturing Reality. https://www.capturingreality.com/Explore-Lasers-Images?utm_source=chatgpt.com (2025).
54. FFmpeg. https://www.ffmpeg.org/ (2025).
55. Luma AI - Interactive Scenes. https://lumalabs.ai/interactive-scenes (2025).
56. Najafi, H. et al. A novel method for 3D lung tumor reconstruction using generative models. Diagnostics 14, 2604 (2024).10.3390/diagnostics14222604 ↗
57. Huttenlocher, D. P., Klanderman, G. A. & Rucklidge, W. J. Comparing images using the Hausdorff distance. IEEE Trans. Pattern Anal. Mach. Intell. 15, 850–863 (1993).10.1109/34.232073 ↗
58. Zhou, Q.-Y., Park, J. & Koltun, V. Open3D: A Modern Library for 3D Data Processing. arXiv:1801.09847 (2018).
59. Virtanen, P. et al. SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).10.1038/s41592-019-0686-2 ↗
60. Kraft, D. Computing the Hausdorff distance of two sets from their signed distance functions. Int. J. Comput. Geom. Appl. 30, 19–49 (2018).10.1142/S0218195920500028 ↗
61. Yastikli, N. Documentation of cultural heritage using digital photogrammetry and laser scanning. J. Cult. Herit. 8, 423–427 (2007).10.1016/j.culher.2007.06.003 ↗
62. Chelaru, B. et al. Integration of point cloud, historical records, and condition assessment data in HBIM. Autom. Constr. 161, 105347 (2024).10.1016/j.autcon.2024.105347 ↗
63. Aricò, M. et al. Three-dimensional documentation and virtual web navigation system for the indoor and outdoor exploration of a complex cultural heritage site. Electronics 13, 2833 (2024).10.3390/electronics13142833 ↗
64. Banitalebi-Dehkordi, A. et al. The effect of frame rate on 3D video quality and bitrate. 3D Res. 6, 1 (2014).10.1007/s13319-014-0034-3 ↗
65. Kumar, R. et al. Video flashlights: real time rendering of multiple videosfor immersive model visualization. video flashlights-real time rendering of multiple videos for immersive model visualization. ACM Int. Conf. Proc. Ser. 28, 157–168 (2002).
66. Rattanarungrot, S. et al. Enhancing cultural heritage accessibility through three-dimensional artifact visualization on web-based open frameworks. Informatics 12, 37 (2025).10.3390/informatics12020037 ↗
67. trimesh 4.7.1 documentation. https://trimesh.org/ (2025).
68. SuperSplat. https://playcanvas.com/supersplat/editor (2025).
69. Lee Enriquez, A. A Contributory Web-Based Application For Documenting Historic Resources: French Colonial Era Art Deco Architecture In Hanoi. MSc thesis, University of Southern California, US (2015).
70. PlayCanvas WebGL Game Engine. https://playcanvas.com/ (2025).