Kitle kaynaklı insansız hava aracı verileri kullanılarak ahşap eserlerin 3B modellenmesi: Truva Atı örneği

Yıl 2022, Cilt: 5 Sayı: 2, 155 - 166, 26.12.2022

Ahmet Uslu , Murat Uysal

https://doi.org/10.33725/mamad.1207416

Cited By: 2

Öz

Günümüzde mobil cihazların, sosyal medya platformlarının ve web tabanlı uygulamaların yaygın kullanımı, kitle kaynak kullanımı adı verilen yeni bir inovasyon paradigmasını mümkün kılmıştır. Ortaya çıkan bu model, geniş bir araştırma yelpazesinde verilerin toplanması ve paylaşılması bakımından yenilikçi bir araç haline gelmiştir. Sosyal medya platformlarında herkese açık olarak paylaşılan fotoğraflar ve videolar, nesnelerin üç boyutlu (3B) gerçekliğe dayalı dijital modellerini oluşturmak için zaman ve maliyet açısından etkin bir fırsat sunmaktadır. Bu çalışmada, Youtube platformundan ücretsiz olarak elde edilen İnsansız Hava Aracı (İHA) verileri ve Hareket ile Nesne Oluşturma (SfM) tekniği kullanılarak Truva Atı’nın 3B modellenmesi amaçlanmıştır. Çalışma, kitle kaynaklı İHA verileri kullanılarak ahşap eserlerin 3B modellenmesi alanında çalışmalar gerçekleştiren araştırmacılara ulaşmayı hedeflemekte, ahşap eserlerin korunması ve gelecek nesillere aktarılması için referans oluşturabilecek yenilikçi bir yaklaşım sunmaktadır. Çalışmanın sonuçları, ahşap eserlerin 3B modellenmesinde kitle kaynaklı İHA verilerinin, veri kaynağı olarak uygunluğunu göstermiştir. Gelecekte, kitle kaynak kullanımının yaygınlaşması ve görüntü kalitesinin daha yüksek çözünürlüklü hale gelmesi, bu tür araştırmalara artan bir ivme kazandıracak ve yeni araştırmaların önünü açacaktır.

Anahtar Kelimeler

Ahşap eser, Kitle kaynaklı veri, İHA, SfM, 3B model

3D modeling of wooden artifacts using crowdsourced unmanned aerial vehicle data: A case study of the Trojan Horse

Öz

Nowadays, the widespread use of mobile devices, social media platforms and web-based applications has enabled a new paradigm of innovation called crowdsourcing. This emerging model has become an innovative tool for collecting and sharing data across a wide range of research. Photos and videos shared publicly on social media platforms offer a time and cost effective opportunity to create three-dimensional (3D) reality-based digital models of objects. In this study, it is aimed to 3D modelling of the Trojan Horse using the Unmanned Aerial Vehicle (UAV) data obtained free of charge from the Youtube platform and the Structure From Motion (SfM) technique. The study aims to reach researchers who work in the field of 3D modelling of wooden artifacts using crowdsourced UAV data, and offers an innovative approach that can be a reference for the conservation and transfer of wooden artifacts to next generation. The results of the study showed the suitability of crowdsourced UAV data as a data source in 3D modelling of wooden artifacts. In the future, widespread use of crowdsourcing and higher resolution image quality will gain increased momentum such research and pave the way for new research.

Anahtar Kelimeler

Wooden artifact, Crowdsourced data, UAV, SfM, 3D model

Kaynakça

• Agarwal, S., Furukawa, Y., Snavely, N., Simon, I., Curless, B., Seitz, S. M., Szeliski, R. (2011), Building rome in a day, Communications of the ACM, 54(10), 105-112. DOI: 10.1109/ICCV.2009.5459148.
• Alsadik, B., Gerke, M., Vosselman, G. (2015), Efficient use of video for 3D modelling of cultural heritage objects. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2(3), 1. DOI: 10.5194/isprsannals-II-3-W4-1-2015.
• Alsadik, B. (2016), Crowdsource and web-published videos for 3D documentation of cultural heritage objects. Journal of Cultural Heritage, 21, 899-903. DOI: 10.1016/j.culher.2016.03.010.
• Alsadik, B. (2022), Crowdsource Drone Imagery–A Powerful Source for the 3D Documentation of Cultural Heritage at Risk. International Journal of Architectural Heritage, 16(7), 977-987. DOI: 10.1080/15583058.2020.1853851.
• Bonacchi, C., Bevan, A., Keinan-Schoonbaert, A., Pett, D., Wexler, J. (2019), Participation in heritage crowdsourcing, Museum Management and Curatorship, 34(2), 166-182. DOI: 10.1080/09647775.2018.1559080.
• Cheng, D., Ch’ng, E. (2022), Harnessing Collective Differences in Crowdsourcing Behaviour for Mass Photogrammetry of 3D Cultural Heritage, ACM Journal on Computing and Cultural Heritage, 1(1): 1–24. https://dl.acm.org/doi/10.1145/3569090.
• Desai, A., Warner, J., Kuderer, N., Thompson, M., Painter, C., Lyman, G., Lopes, G. (2020), Crowdsourcing a crisis response for COVID-19 in oncology. Nature Cancer, 1(5), 473-476. DOI: 10.1038/s43018-020-0065-z.
• Dhonju, H. K., Xiao, W., Shakya, B., Mills, J. P., Sarhosis, V. (2017), Documentation of heritage structures through geo-crowdsourcing and webmapping. In The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 17-21, DOI: 10.5194/isprs-archives-XLII-2-W7-17-2017.
• Frahm, J. M., Pollefeys, M., Lazebnik, S., Gallup, D., Clipp, B., Raguram, R., Johnson, T. (2010), Fast robust large-scale mapping from video and internet photo collections. ISPRS Journal of Photogrammetry and Remote Sensing, 65(6), 538-549. DOI: 10.1016/j.isprsjprs.2010.08.009.
• Gkeli, M., Potsiou, C., Ioannidis, C. (2020), A technical solution for 3D crowdsourced cadastral surveys, Land use policy, 98, 104419. DOI: 10.1016/j.landusepol. 2019.104419.
• Gong, Y., van Engelenburg, S., Janssen, M. (2021), A reference architecture for blockchain-based crowdsourcing platforms, Journal of Theoretical and Applied Electronic Commerce Research, 16(4), 937-958, DOI: 10.3390/jtaer16040053.
• Johnson, B. P., Dayan, E., Censor, N., Cohen, L. G. (2022), Crowdsourcing in cognitive and systems neuroscience, The Neuroscientist, 28(5), 425-437. DOI: 10.1177/ 10738584211017018.
• Li, L., Tang, L., Zhu, H., Zhang, H., Yang, F., Qin, W. (2017), Semantic 3D modeling based on CityGML for ancient Chinese-style architectural roofs of digital heritage, ISPRS International Journal of Geo-Information, 6(5), 132. DOI: 10.3390/ijgi6050132.
• Litman, L., Robinson, J., Abberbock, T. (2017), TurkPrime. com: A versatile crowdsourcing data acquisition platform for the behavioral sciences. Behavior research methods, 49(2), 433-442, DOI: 10.3758/s13428-016-0727-z.
• Liu, Z., Brigham, R., Long, E. R., Wilson, L., Frost, A., Orr, S. A., Grau-Bové, J. (2022), Semantic segmentation and photogrammetry of crowdsourced images to monitor historic facades, Heritage Science, 10(1), 1-17. DOI: 10.1186/s40494-022-00664-y.
• Mao, K., Capra, L., Harman, M., Jia, Y. (2017), A survey of the use of crowdsourcing in software engineering, Journal of Systems and Software, 126, 57-84. DOI: 10.1016/j.jss.2016.09.015.
• Shishido, H., Ito, Y., Kawamura, Y., Matsui, T., Morishima, A., Kitahara, I. (2017), Proactive preservation of world heritage by crowdsourcing and 3D reconstruction technology, In 2017 IEEE International Conference on Big Data (Big Data), pp. 4426-4428, IEEE. DOI: 10.1109/BigData.2017.8258479.
• Snavely, N., Seitz, S. M., Szeliski, R. (2006), Photo tourism: exploring photo collections in 3D, In ACM siggraph 2006 papers, 835-846. DOI: 10.1145/1141911.1141964.
• Somogyi, A., Barsi, A., Molnar, B., Lovas, T. (2016), Crowd sourcing based 3d modeling. International Archives of Photogrammetry, Remote Sensing & Spatial Information Sciences, 41(B5). DOI: 10.5194/isprs-archives-XLI-B5-587-2016.
• Stathopoulou, E. K., Georgopoulos, A., Panagiotopoulos, G., Kaliampakos, D. (2015), Crowdsourcing Lost Cultural Heritage. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 2, DOI:10.5194/isprsannals-II-5-W3-295-2015.
• Themistocleous, K. (2017), Model reconstruction for 3D visualization of cultural heritage sites using open data from social media: The case study of Soli, Cyprus. Journal of Archaeological Science: Reports, 14, 774-781. DOI: 10.1016/j.jasrep.2016.08.045.
• Tabib, R.A., Santoshkumar, T., Pradhu, V., Patil, U., Mudenagudi, U. (2021), Categorization and Selection of Crowdsourced Images Towards 3D Reconstruction of Heritage Sites. In Digital Techniques for Heritage Presentation and Preservation, pp. 133-146, Springer, Cham. DOI: 1007/978-3-030-57907-4_7.
• Tong, Y., Zhou, Z., Zeng, Y., Chen, L., Shahabi, C. (2020), Spatial crowdsourcing: a survey. The VLDB Journal, 29(1), 217-250. DOI: 10.1007/s00778-019-00568-7.
• Uslu A., Uysal, M. (2021), Kitle kaynaklı fotoğraflar kullanılarak kültürel mirasın üç boyutlu modellenmesi ve web tabanlı görselleştirilmesi: Afrodisias-Tetrapylon örneği. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, 21(3), 632-639. DOI: 10.35414/akufemubid.889211.
• Vincent, M. L., Gutierrez, M. F., Coughenour, C., Manuel, V., Bendicho, L. M., Remondino, F., Fritsch, D. (2015), Crowd-sourcing the 3D digital reconstructions of lost cultural heritage, In 2015 Digital Heritage,1, 171-172. DOI: 10.1109/DigitalHeritage. 2015.7413863.
• Wahbeh, W., Nebiker, S., Fangi, G. (2016), Combining public domain and professional panoramic imagery for the accurate and dense 3D reconstruction of the destroyed bel temple in Palmyra, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 3, 81, DOI: 10.5194/isprs-annals-III-5-81-2016.
• Wu, C. (2011), VisualSFM: A visual structure from motion system. http://www. cs. washington. edu/homes/ccwu/vsfm.
• Xue, Y., Zhang, S., Zhou, M., Zhu, H. (2021), Novel SfM-DLT method for metro tunnel 3D reconstruction and Visualization, Underground Space, 6(2), 134-141. DOI: 10.1016/j.undsp.2020.01.002.
• Yuen, M. C., King, I., Leung, K. S. (2011), A survey of crowdsourcing systems, In 2011 IEEE third international conference on privacy, security, risk and trust and 2011 IEEE third international conference on social computing, 766-773, DOI: 10.1109/PASSAT/SocialCom.2011.203.
• Zhou, B., Ma, W., Li, Q., El-Sheimy, N., Mao, Q., Li, Y., Zhu, J. (2021), Crowdsourcing-based indoor mapping using smartphones: A survey. ISPRS Journal of Photogrammetry and Remote Sensing, 177, 131-146. DOI: 10.1016/j.isprsjprs.2021.05.006.
• URL 1 (2022), Troya Atı, https://www.kulturportali.gov.tr/portal/troyaati, Son erişim tarihi: 05.11.2022.
• URL 2 (2022), Youtube videosu, https://www.youtube.com/watch?v=Cy6FS0MSgkA, Son erişim tarihi: 08.11.2022.
• URL 3 (2022), KMPlayer, https://www.kmplayer.com/home, Son erişim tarihi: 08.11.2022.
• URL 4 (2022), VisualSFM, http://ccwu.me/vsfm/index.html, Son erişim tarihi: 09.11.2022.
• URL 5 (2022), CloudCompare, https://www.danielgm.net/cc, Son erişim tarihi: 09.11.2022.