Rapid 3D Digitization of Cultural Heritage Objects for Tourism Applications Using iPad LiDAR

The tourism industry increasingly relies on digital technologies to enrich visitor experiences, preserve cultural heritage, and strengthen destination marketing. Traditional promotional methods are often insufficient, while augmented reality (AR), virtual reality (VR), and digital twin applications are emerging as transformative tools. For these technologies to succeed, rapid, portable, and low-cost three-dimensional (3D) data acquisition methods are essential. Light Detection and Ranging (LiDAR) has long been recognized for its precision in generating dense point clouds; however, conventional terrestrial systems are expensive, bulky, and require expert operation. The iPad’s built-in LiDAR sensor offers a practical alternative with portability, affordability, and ease of use, enabling fast and on-site digitization of cultural heritage assets. In this study, five representative objects were documented during a city tour using iPad LiDAR and processed through Reconstructor® for point cloud filtering and mesh generation. The resulting 3D models included: (1) a human statue (~0.8 × 0.8 × 1.6 m), (2) a violinist statue (~0.9 × 0.7 × 1.7 m), (3) a seated group around a table (~1.8 × 1.6 × 1.6 m), (4) an ancient stone pedestal (~1.2 × 1.0 × 0.8 m), and (5) an elderly couple on a bench (~1.8 × 0.8 × 1.5 m). Scanning sessions lasted 3–6 minutes, with processing times between 7–15 minutes. Point clouds contained between 480,000 and 1.25 million points, while meshes ranged from 85,000 to 210,000 faces. To quantitatively evaluate geometric quality, three core analyses were performed in CloudCompare (v2.12): (i) Roughness Analysis to measure local surface irregularities, (ii) Normal Deviation Analysis to assess orientation stability, and (iii) Point-to-Mesh (C2M) Distance Analysis to determine geometric accuracy and consistency. Across all models, mean roughness values ranged from 4.1–7.4 mm, normal deviations from 7°–27°, and C2M median accuracy from 4.8–7.9 mm, demonstrating that post-processing effectively compensates for raw LiDAR noise and alignment drift. These results confirm that iPad LiDAR can generate complete and metrically coherent 3D reconstructions in under 15 minutes per object, balancing practicality and geometric fidelity. The workflow offers a reproducible and accessible solution for rapid cultural heritage digitization, supporting immersive tourism experiences and the broader digital transformation of heritage preservation, interpretation, and public engagement.