This research evaluates the potential of the Apple’s LiDAR sensor for capturing indoor environments using multiple software solutions. The study investigates the accuracy and reliability of iPhone-generated point clouds by comparing them against survey-grade Terrestrial Laser Scanner (TLS) data. Two primary evaluation methods are employed: Cloud-to-Cloud (C2C) comparison and Control Point Approach (CPA). C2C analysis utilizes iterative closest point (ICP) algorithms to assess the overall geometric agreement between the iPhone and TLS point clouds. The CPA involves comparing the coordinates of precisely measured target points within both datasets to determine positional accuracy. Furthermore, the research analyses the noise levels present in the iPhone point clouds and evaluates the point density achieved by the device. This comprehensive assessment aims to determine the suitability of the iPhone’s LiDAR for various indoor mapping applications, considering factors like data quality, processing efficiency, and achievable accuracy compared to traditional surveying methods. The results highlight the strengths and limitations of using mobile LiDAR for indoor surveys and provide insights into optimal workflows and software choices for maximizing data quality.