Abstract

This study evaluates the geometric accuracy of point clouds acquired with Apple’s TrueDepth sensor, focusing on the influence of sensor-to-object distance. Tests were conducted using Necuron 651 polyurethane specimens featuring a flat surface, two parallel planes, and a hemisphere. A semi-automated test bench enabled digitization at different stand-off distances. The resulting point clouds were analyzed using geometric and dimensional indicators, including flatness, parallelism, hemispherical profile accuracy, interplane distance, and sphere diameter.

The results provide insights into the influence of stand-off distance on reconstruction accuracy and help assess the suitability of low-cost TrueDepth sensing for applications requiring reliable geometric measurements, particularly in biomedical engineering and personalized healthcare.

Keywords

3D scanning, Low-cost sensors, Reverse engineering, Quality