iPhone LiDAR Tabanlı 3B Modellemenin Küçük Boyutlu Nesneler İçin Doğruluğunun Değerlendirilmesi

Yıl 2025, Cilt: 7 Sayı: 2, 60 - 71

Seda Nur Gamze Hamal , Ebruhayat Civelekoğlu

Öz

Bu çalışma, küçük ölçekli ve detay seviyesi yüksek nesnelerin üç boyutlu (3B) olarak modellenmesinde iPhone 16 Pro Max Light Detection and Ranging (LiDAR) sensörünün performansını yersel fotogrametri yöntemiyle karşılaştırmalı olarak değerlendirmektedir. Yüzey geometrisi ve doku karmaşıklığı bakımından farklı üç obje üzerinde yürütülen deneysel çalışma kapsamında, her iki yöntemden elde edilen modeller görsel kalite, ölçümsel doğruluk ve yüzey fark analizleri Cloud-to-Cloud (C2C) üzerinden incelenmiştir. Fotogrametrik modeller yüksek çözünürlüklü görüntülere dayalı olarak ayrıntılı yüzey geometrisini başarıyla temsil etmiş ve RMSE değerleri 0,152–0,168 cm aralığında gerçekleşmiştir. Buna karşılık iPhone LiDAR sensörü hızlı veri üretimi ve yüksek taşınabilirlik avantajları sunmakla birlikte, küçük nesne ölçeğinde derinlik çözünürlüğü ve nokta yoğunluğuna bağlı sınırlılıklar göstermiş; RMSE değerleri 0,240–0,274 cm aralığında seyretmiştir. C2C analizleri özellikle karmaşık yüzeylerde LiDAR sapmalarının arttığını ve ince detayların düzleştiğini ortaya koymuştur. Bulgular, mobil LiDAR sistemlerinin küçük nesnelerin hızlı ön-belgelenmesi ve orta düzey detay gerektiren uygulamalar için uygun olmakla birlikte, yüksek doğruluk gerektiren kültürel miras, arkeolojik buluntu veya laboratuvar ölçekli modellemelerde fotogrametrinin daha güvenilir bir yöntem olduğunu göstermektedir. Çalışma ayrıca sensör çözünürlüğü, yazılım temelli işleme teknikleri ve hibrit modelleme yaklaşımlarının gelecekte küçük ölçekli modelleme performansını iyileştirmeye yönelik önemli araştırma alanları sunduğunu vurgulamaktadır.

Anahtar Kelimeler

iPhone LiDAR , Fotogrametri , Structure-from-Motion (SfM) , (C2C) analizi

Etik Beyan

Yapılan çalışmada araştırma ve yayın etiğine uyulmuştur.

Evaluation of iPhone LiDAR-Based 3D Modeling Accuracy for Small-Sized Objects

Öz

This study comparatively evaluates the performance of the iPhone 16 Pro Max Light Detection and Ranging (LiDAR) sensor and terrestrial photogrammetry for three-dimensional (3D) modeling of small-sized objects with a high level of geometric detail. An experimental framework was conducted using three objects exhibiting different surface geometries and texture complexities, and the resulting models were assessed in terms of visual quality, metric accuracy, and surface deviation through Cloud-to-Cloud (C2C) analysis. Photogrammetric models, generated from high-resolution imagery, successfully represented fine surface geometry, yielding RMSE values in the range of 0.152–0.168 cm. In contrast, while the iPhone LiDAR sensor provides advantages in rapid data acquisition and high portability, it exhibited limitations related to depth resolution and point density at the small-object scale, with RMSE values ranging from 0.240 to 0.274 cm. C2C analyses revealed increased deviations in LiDAR-derived models, particularly over geometrically complex surfaces, where fine details tended to be smoothed or flattened. The findings indicate that although mobile LiDAR systems are suitable for rapid preliminary documentation of small objects and applications requiring a moderate level of detail, photogrammetry remains a more reliable approach for high-accuracy applications such as cultural heritage documentation, archaeological artifact recording, and laboratory-scale modeling. The study further highlights sensor resolution, software-based processing strategies, and hybrid modeling approaches as key research directions for improving small-scale 3D modeling performance in future studies.

Anahtar Kelimeler

iPhone LiDAR , Photogrammetry , Structure-from-Motion (SfM) , Cloud-to-Cloud (C2C) analysis

Kaynakça

• Apple Inc. (2024). iPhone 16 Pro Max – Technical Specifications. Apple Official Website.
• Balado, J., Garozzo, R., Winiwarter, L., & Tilon, S. (2025). A systematic literature review of low-cost 3D mapping solutions. Information Fusion, 114, 102656. https://doi.org/10.1016/j.inffus.2024.102656
• Beavers, C., Day, C., Krietemeyer, A., Peterson, S. M., Ahn, Y., & Li, X. (2024). Mapping of Pavement Conditions Using Smartphone/Tablet LiDAR Case Study: Sensor Performance Comparison (No. 24-16). San Jose State University. College of Business. Mineta Transportation Institute. https://doi.org/10.31979/mti.2024.2224
• Cuperschmid, A. R. M., Neves de Oliveira, G., & Froner, Y. A. (2025). Exploring the use of lidar in smartphones: documenting the frontispiece of Saint Francis of Assisi Church in Ouro Preto, Brazil. International Journal of Architectural Heritage, 19(9), 1397-1414. https://doi.org/10.1080/15583058.2024.2344163
• De Paolis, L. T., De Luca, V., Gatto, C., D’Errico, G., & Paladini, G. I. (2020, August). Photogrammetric 3D reconstruction of small objects for a real-time fruition. In International Conference on Augmented Reality, Virtual Reality and Computer Graphics (pp. 375-394). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-58465-8_28
• Dostal, C., & Yamafune, K. (2018). Photogrammetric texture mapping: A method for increasing the Fidelity of 3D models of cultural heritage materials. Journal of Archaeological Science: Reports, 18, 430-436. https://doi.org/10.1016/j.jasrep.2018.01.024
• Galantucci, L. M., Guerra, M. G., & Lavecchia, F. (2018, May). Photogrammetry applied to small and micro scaled objects: a review. In International Conference on the Industry 4.0 Model for Advanced Manufacturing (pp. 57-77). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-89563-5_4
• Grobler, E., & Celano, G. (2025). Photogrammetric and LiDAR Scanning with iPhone 13 Pro: Accuracy, Precision and Field Application on Hazelnut Trees. Sensors, 25(18), 5629. https://doi.org/10.3390/s25185629
• Iglhaut, J., Cabo, C., Puliti, S., Piermattei, L., O’Connor, J., & Rosette, J. (2019). Structure from motion photogrammetry in forestry: A review. Current Forestry Reports, 5(3), 155-168. https://doi.org/10.1007/s40725-019-00094-3
• Kovanič, Ľ., Peťovský, P., Topitzer, B., & Blišťan, P. (2024). Spatial analysis of point clouds obtained by SfM photogrammetry and the TLS method—Study in quarry environment. Land, 13(5), 614. https://doi.org/10.3390/land13050614
• Krausková, D., Mikita, T., Hrůza, P., & Kudrnová, B. (2025). Accuracy Assessment of iPhone LiDAR for Mapping Streambeds and Small Water Structures in Forested Terrain. Sensors, 25(19), 6141. https://doi.org/10.3390/s25196141
• Kuçak, R. A., Erol, S., & Alkan, R. M. (2023). iPad Pro LiDAR sensörünün profesyonel bir yersel lazer tarayıcı ile karşılaştırmalı performans analizi. Geomatik, 8(1), 35-41. https://doi.org/10.29128/geomatik.1105048
• Łabędź, P., Skabek, K., Ozimek, P., Rola, D., Ozimek, A., & Ostrowska, K. (2022). Accuracy verification of surface models of architectural objects from the iPad LiDAR in the context of photogrammetry methods. Sensors, 22(21), 8504. https://doi.org/10.3390/s22218504
• Luetzenburg, G., Kroon, A., & Bjørk, A. A. (2021). Evaluation of the Apple iPhone 12 Pro LiDAR for an application in geosciences. Scientific reports, 11(1), 1-9.
• Mach, J., Svatý, Z., Šoupa, O., Nouzovský, L., & Halecký, M. (2025). Implementation of an SfM-MVS-based photogrammetry approach for detailed 3D reconstruction of plants. Plant Methods, 21(1), 127. https://doi.org/10.1186/s13007-025-01445-x
• Maté-González, M. Á., Yali, R., Rodríguez-Hernández, J., González-González, E., & Aguirre de Mata, J. (2025). Comparison of NeRF-and SfM-Based Methods for Point Cloud Reconstruction for Small-Sized Archaeological Artifacts. Remote Sensing, 17(14), 2535. https://doi.org/10.3390/rs17142535
• Morita, M. M., Carvajal, D. A. L., Bagur, I. L. G., & Bilmes, G. M. (2024). A combined approach of SFM-MVS photogrammetry and reflectance transformation imaging to enhance 3D reconstructions. Journal of Cultural Heritage, 68, 38-46. https://doi.org/10.1016/j.culher.2024.05.008
• Paukkonen, N. (2023). Towards a mobile 3D documentation Solution. Video-based photogrammetry and iPhone 12 Pro as fieldwork documentation tools. Journal of Computer Applications in Archaeology, 6(1). https://doi.org/10.5334/jcaa.135
• Qiu, Z., Martínez-Sánchez, J., Brea, V. M., Lopez, P., & Arias, P. (2022). Low-cost mobile mapping system solution for traffic sign segmentation using Azure Kinect. International Journal of Applied Earth Observation and Geoinformation, 112, 102895. https://doi.org/10.1016/j.jag.2022.102895
• Sanz-Ablanedo, E., Chandler, J. H., Rodríguez-Pérez, J. R., & Ordóñez, C. (2018). Accuracy of unmanned aerial vehicle (UAV) and SfM photogrammetry survey as a function of the number and location of ground control points used. Remote Sensing, 10(10), 1606. https://doi.org/10.3390/rs10101606
• Scaggion, C., Castelli, S., Usai, D., & Artioli, G. (2022). 3D digital dental models’ accuracy for anthropological study: Comparing close-range photogrammetry to μ-CT scanning. Digital Applications in Archaeology and Cultural Heritage, 27, e00245. https://doi.org/10.1016/j.daach.2022.e00245
• Schöps, T., Sattler, T., Häne, C., & Pollefeys, M. (2015, October). 3D modeling on the go: Interactive 3D reconstruction of large-scale scenes on mobile devices. In 2015 International Conference on 3D Vision (pp. 291-299). IEEE. https://doi.org/10.1109/3DV.2015.40
• Soyluoğlu, M., Orabi, R., Hermon, S., & Bakirtzis, N. (2025). Digitizing Challenging Heritage Sites with the Use of IPhone LiDAR and Photogrammetry: The Case-Study of Sourp Magar Monastery in Cyprus. Geomatics, 5(3), 44. https://doi.org/10.3390/geomatics5030044
• Tatsumi, S., Yamaguchi, K., & Furuya, N. (2023). ForestScanner: A mobile application for measuring and mapping trees with LiDAR‐equipped iPhone and iPad. Methods in Ecology and Evolution, 14(7), 1603-1609. https://doi.org/10.1111/2041-210X.13900
• Ulvi, A., & Hamal, S. N. G. (2025). Fusion of IPAD Pro LiDAR and SfM-Based Photogrammetry for 3D Documentation of Cultural Heritage. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 1-17. https://doi.org/10.1007/s40996-025-01936-w
• 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. https://doi.org/10.1016/j.geomorph.2012.08.021
• Xing, Y., Yang, S., Fahy, C., Harwood, T., & Shell, J. (2025). Capturing the Past, Shaping the Future: A Scoping Review of Photogrammetry in Cultural Building Heritage. Electronics, 14(18), 3666. https://doi.org/10.3390/electronics14183666
• Yiğit, A. Y., & Kaya, Y. (2025). Augmented Reality and Photogrammetry Based Anatomical Models in Medical Education. SN Computer Science, 6(6), 667. https://doi.org/10.1007/s42979-025-04218-4
• Yiğit, A. Y., & Ulvi, A. (2025). Kentsel planlama ve çevresel izleme için fotogrametri tabanlı ağaç modelleme. Anadolu Orman Araştırmaları Dergisi, 11(1), 60-71. https://doi.org/10.53516/ajfr.1611711
• Yiğit, A. Y., & Uysal, M. (2025). Virtual reality visualisation of automatic crack detection for bridge inspection from 3D digital twin generated by UAV photogrammetry. Measurement, 242, 115931. https://doi.org/10.1016/j.measurement.2024.115931
• Zeybek, M. (2024). Akıllı Telefon iPhone LiDAR Tarayıcısının Altyapı Çalışmalarında Uygulanabilirliği. Turkey Lidar Journal/Türkiye Lidar Dergisi, 6(1). https://doi.org/10.51946/melid.1402883
• Zhang, N., & Lan, X. (2024). Everyday-Carry Equipment Mapping: A Portable and Low-Cost Method for 3D Digital Documentation of Architectural Heritage by Integrated iPhone and Microdrone. Buildings, 15(1), 89. https://doi.org/10.3390/buildings15010089