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

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

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

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