Evaluation of the Apple iPhone 12 Pro LiDAR for an Application in Geosciences

Abstract

Traditionally, topographic surveying in earth sciences requires high financial investments, elaborate logistics, complicated training of staff and extensive data processing. Recently, off-the-shelf drones with optical sensors already reduced the costs for obtaining a high-resolution dataset of an Earth surface considerably. Nevertheless, costs and complexity associated with topographic surveying are still high. In 2020, Apple Inc. released the iPad Pro 2020 and the iPhone 12 Pro with novel build-in LiDAR sensors. Here we investigate the basic technical capabilities of the LiDAR sensors and we test the application at a coastal cliff in Denmark. The results are compared to state-of-the-art Structure from Motion Multi-View Stereo (SfM MVS) point clouds. The LiDAR sensors create accurate high-resolution models of small objects with a side length > 10 cm with an absolute accuracy of ± 1 cm. 3D models with the dimensions of up to 130 × 15 × 10 m of a coastal cliff with an absolute accuracy of ± 10 cm are compiled. Overall, the versatility in handling outweighs the range limitations, making the Apple LiDAR devices cost-effective alternatives to established techniques in remote sensing with possible fields of application for a wide range of geo-scientific areas and teaching.

Figure 1

Workflow for the acquisition of the LiDAR point clouds with the ‘3d Scanner App’ (red box), the SfM MVS point clouds (green box) including fieldwork (purple box), and the Multi-Scale Model-to-Model Cloud Comparison (M3C2) in CloudCompare (blue box).

Figure 2

The Apple iPhone 12 Pro mounted on a selfie stick with the LiDAR sensor emitting an array of 8 × 8 points diffracted into 3 × 3 grids making a total of 576 points (a), Apple iPhone 12 Pro camera module (b), 3D model of an object with measured dimensions (c), 3d Scanner App scanning deadfall at Roneklint on the Apple iPhone 12 Pro (Photo credit Kent Pørksen) (d).

Figure 3

Percentage of accuracy between measured and real values of small objects for length, width and height. Lines show precision of measured values for repeated scans (n = 5) of the same object. Accuracy and precision are increasing with object size in all directions. Lines indicate linear trend lines.

Figure 4

M3C2 distances in meter between SfM MVS reference point cloud and iPhone point cloud, fine registration error RMS: 0.052 m computed on 5 million points with a theoretical overlap: 75%, point clouds subsampled to 0.05 m minimal nominal spacing between points with normal directions and projection diameter calculated at 1.33 m for each point (a), textured iPhone LiDAR model of the cliff (b) iPhone LiDAR hillshade model of the cliff (c). Scale bar in bottom right indicates 35 m.