Research on 3D reconstruction of small size objects using structure from motion photogrammetry via smartphone images

Year 2023, Volume: 10 Issue: 2, 112 - 123, 01.11.2023

Duygu Arıcan Ferat Furkan Göksu Nursu Tunalıoğlu Taylan Öcalan

https://doi.org/10.9733/JGG.2023R0008.E

Abstract

According to the recent developments on image acquisition via relatively low-cost devices such as smartphones or tablets, researches on Structure-from-Motion (SfM) photogrammetry-based 3 dimensional (3D) model reconstruction become more popular and wide frequent practice especially for study areas in geology/geomorphology, historical heritage, forestry, etc. Thus, this paper demonstrates accuracy assessment of SfM photogrammetry-based 3D model reconstruction of small size objects by altering number of the source imagery captured by smartphone and represents change detection of the generated datasets as (i) cloud-to-cloud and (ii) mesh-to-cloud comparisons. The number of images was decreased as 25% of each datasets belonging three different detail types of small size objects as the Safranbolu miniature house (SMH), a trinket made of a combination of historical buildings in Rome (HBR), and a wooden object (WO). A total of 12 datasets were generated and 9 cloud-to-cloud, and 21 mesh-to-cloud comparisons were performed. Since the obtained results show that the quality of 3D models of objects varies according to their shapes and sizes, change detection analyses show that the detail level of the objects are highly correlated with the resultant model accuracy.

Keywords

Structure-from-Motion (SfM) photogrammetry, Smartphone imagery, 3D Model reconstruction, Change detection

Thanks

Duygu Arıcan, YÖK 100/2000 Doktora Programı kapsamında Yükseköğretim Kurulu (YÖK) tarafından belirlenen 100 ulusal öncelikli alandan biri olan "CBS ve Bilişim Uygulamaları" alanında YÖK bursiyeridir. Duygu Arıcan is a Ph.D. scholarship holder from the Council of Higher Education (YÖK) in the field of "GIS and Informatics Applications", which is one of the 100 national priority areas determined by YÖK within the scope of the YÖK 100/2000 Doctorate Program.

Küçük boyutlu nesnelerin 3 boyutlu modellemesi için akıllı telefon görüntüleri kullanılarak hareketten nesne oluşturma fotogrametrisine dayalı bir araştırma

Abstract

Son yıllarda, akıllı telefon ya da tabletler gibi nispeten düşük maliyetli cihazlarla görüntü elde etme alanında yaşanan gelişmeler sonucunda, Hareketten Nesne Oluşturma (HNO) fotogrametrisine dayalı 3 boyutlu (3B) modelleme popüler olmuş ve özellikle jeoloji/jeomorfoloji, kültürel miras, orman vb. alanlarda yaygın bir uygulama haline gelmiştir. Bu çalışmanın amacı, akıllı telefon kamerası ile elde edilen görüntülerden değişen görüntü sayılarına dayalı 3B model oluşturma potansiyelinin araştırılmasıdır. Bu amaçla, küçük boyutlu nesnelere ait akıllı telefon kameraları kullanılarak elde edilen farklı sayıdaki görüntüler kullanılarak HNO fotogrametrisine dayalı 3B veri setleri oluşturulmuş ve bu veri setleri için (i) nokta bulutları arasında, (ii) üçgen model ile nokta bulutu arasında, olmak üzere sapma analizleri gerçekleştirilmiştir. Safranbolu minyatürü (SMH), Roma biblosu (HBR) ve ahşap nesne (WO) olmak üzere farklı detay düzeylerinde seçilen bu üç farklı boyuttaki nesne için elde edilen görüntüler her veri setinde %25 oranında azaltılmıştır. Bu şekilde, toplam 12 farklı veri seti oluşturulmuş ve bu veri setleri için nokta bulutları arasında 9, üçgen model ile nokta bulutu arasında ise 21 farklı karşılaştırma gerçekleştirilmiştir. Buna göre, elde edilen sonuçlar dikkate alındığında 3B modellerin doğruluğunun, modellemesi yapılan nesnelerin şekilsel ve boyutsal farklılığına göre değiştiği görülmekle birlikte, sapma analizine dayalı elde edilen sonuçlar nesnenin detay düzeyi ile sonuç model doğruluğu arasında yüksek ilişki olduğunu göstermiştir.

Keywords

Hareketten nesne oluşturma (HNO), Akıllı telefon görüntüsü, 3B Model oluşturma, Değişim analizi

References

• Barszcz, M., Montusiewicz, J., Paśnikowska-Łukaszuk, M., & Sałamacha, A. (2021). Comparative analysis of digital models of objects of cultural heritage obtained by the “3D SLS” and “SfM” methods. Applied Sciences, 11(12), 5321.
• Carbonneau, P. E., Lane, S. N., & Bergeron, N. E. (2003). Cost-effective non-metric close-range digital photogrammetry and its application to a study of coarse gravel river beds. International journal of remote sensing, 24(14), 2837-2854.
• Chandler, J., Ashmore, P., Paola, C., Gooch, M., & Varkaris, F. (2002). Monitoring river‐channel change using terrestrial oblique digital imagery and automated digital photogrammetry. Annals of the Association of American Geographers, 92(4), 631-644.
• Chen, H., Xu, F., Liu, W., Sun, D., Liu, P. X., Menhas, M. I., & Ahmad, B. (2021). 3D reconstruction of unstructured objects using information from multiple sensors. IEEE Sensors Journal, 21(23), 26951-26963.
• Clini, P., Frapiccini, N., Mengoni, M., Nespeca, R., & Ruggeri, L. (2016). SFM Technique and Focus Stacking for Digital Documentation of Archaeological Artifacts. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 41B5, 229–236.
• Collins, T., Woolley, S. I., Gehlken, E., & Ch’ng, E. (2019). Automated low-cost photogrammetric acquisition of 3D models from small form-factor artefacts. Electronics, 8(12), 1441.
• De Reu, J., De Smedt, P., Herremans, D., Van Meirvenne, M., Laloo, P., & De Clercq, W. (2014). On introducing an image-based 3D reconstruction method in archaeological excavation practice. Journal of Archaeological Science, 41, 251-262.
• Ding, Y., Zheng, X., Zhou, Y., Xiong, H., & Gong, J. (2018). Low-cost and efficient indoor 3D reconstruction through annotated hierarchical structure-from-motion. Remote Sensing, 11(1), 58.
• Furukawa, Y., Curless, B., Seitz, S. M., & Szeliski, R. (2010). Towards internet-scale multi-view stereo. 2010 IEEE computer society conference on computer vision and pattern recognition, 1434-1441.
• Guidi, G., Micoli, L. L., Gonizzi, S., Brennan, M., & Frischer, B. (2015). Image-based 3D capture of cultural heritage artifacts an experimental study about 3D data quality. 2015 Digital Heritage, 321-324.
• Jasińska, A., Pyka, K., Pastucha, E., & Midtiby, H. S. (2023). A Simple Way to Reduce 3D Model Deformation in Smartphone Photogrammetry. Sensors, 23(2), 728.
• Jeong, H., Ahn, H., Shin, D., Ahn, Y., & Choi, C. (2019). A Comparative Assessment of the Photogrammetric Accuracy of Mapping Using UAVs with Smart Devices. Photogramm. Eng. Remote Sens, 85, 889-897.
• Kazhdan, M., & Hoppe, H. (2013). Screened poisson surface reconstruction. ACM Transactions on Graphics (ToG), 32(3), 1-13.
• Lane, S. N., Widdison, P. E., Thomas, R. E., Ashworth, P. J., Best, J. L., Lunt, I. A., Sambrook, S., & Simpson, C. J. (2010). Quantification of braided river channel change using archival digital image analysis. Earth Surface Processes and Landforms, 35(8), 971-985.
• Lowe, D. G. (1999). Object recognition from local scale-invariant features. Proceedings of the seventh IEEE international conference on computer vision, 1150-1157.
• Mahami, H., Nasirzadeh, F., Hosseininaveh Ahmadabadian, A., & Nahavandi, S. (2019). Automated progress controlling and monitoring using daily site images and building information modelling. Buildings, 9(3), 70.
• Mali, V. K., & Kuiry, S. N. (2018). Assessing the accuracy of high-resolution topographic data generated using freely available packages based on SfM-MVS approach. Measurement, 124, 338-350.
• Micheletti, N., Chandler, J. H., & Lane, S. N. (2015). Investigating the geomorphological potential of freely available and accessible structure‐from‐motion photogrammetry using a smartphone. Earth Surface Processes and Landforms, 40(4), 473-486.
• Prosdocimi, M., Burguet, M., Di Prima, S., Sofia, G., Terol, E., Comino, J. R., Cerdà, A., & Tarolli, P. (2017). Rainfall simulation and Structure-from-Motion photogrammetry for the analysis of soil water erosion in Mediterranean vineyards. Science of the Total Environment, 574, 204-215.
• Tanskanen, P., Kolev, K., Meier, L., Camposeco, F., Saurer, O., & Pollefeys, M. (2013). Live metric 3D reconstruction on mobile phones. Proceedings of the IEEE International Conference on Computer Vision, 65-72.
• Westoby, M. J., Brasington, J., Glasser, N. F., Hambrey, M. J., & Reynolds, J. M. (2012). ‘Structure-from-Motion’photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology, 179, 300-314.
• Wróżyński, R., Pyszny, K., Sojka, M., Przybyła, C., & Murat-Błażejewska, S. (2017). Ground volume assessment using’Structure from Motion’photogrammetry with a smartphone and a compact camera. Open geosciences, 9(1), 281-294.
• Yang, M. D., Chao, C. F., Huang, K. S., Lu, L. Y., & Chen, Y. P. (2013). Image-based 3D scene reconstruction and exploration in augmented reality. Automation in Construction, 33, 48-60.
• Yao, S., AliAkbarpour, H., Seetharaman, G., & Palaniappan, K. (2018). 3D patch-based multi-view stereo for high-resolution imagery. Geospatial Informatics, Motion Imagery, and Network Analytics VIII 10645, 146-153.