Table 1
Technical details of data capture devices. Technical details on the iPhone LiDAR sensor are not shared by Apple. The listed details are derived from tests by Luetzenburg, Kroon and Bjørk (2021). Since they tested an earlier version of the device, the iPhone 12 versus our iPhone 15, these details may differ.
| DEVICE | TECHNIQUE | SENSOR | ACCURACY | RESOLUTION/PRECISION | EFFECTIVE CAPTURE RANGE | PRICE DEVICE € (2025) | PRICE SOFTWARE |
|---|---|---|---|---|---|---|---|
| iPhone(Sony IMX 591 LiDAR SPAD) | LiDAR with sensor fusion (multiple device sensors) | VCSEL (emitter), SPAD (receiver) Fusion with other device sensors (camera lenses, accelerometer, gyroscope, and magnetometer | 1–10 cm depending on distance to subject | 0.01 megapixel | 0.3–5.0 m | Ca. 1.5k | Free 12,50 p/m (basic license) |
| Nikon D5300 | Photogrammetry | Size: APS-C (23.5 × 15.6 mm) Type: CMOS | Relies on external measurement | 24 megapixels/depends on distance | 0.4–100 m | Ca. 1k | 529 (educational) 3370 (regular) |
| Artec Leo | Structured light | VCSEL (pattern emitter), depth and texture camera specs not provided | 0.1 mm point | 0.2 mm point | 0.35–1.2 m | Ca. 30k (educational) | 2 years included (educational pack) 100 p/m (regular subscription) |
Figure 1
3D data acquisition in the field. Upper left: the Artec Leo. Upper right: the iPhone (photos Jitte Waagen). Lower: positions of the photos captured for photogrammetry.
Table 2
Data capture details.
| DEVICE | CAPTURE SETTINGS | CAPTURE TIMES | DATA SIZE |
|---|---|---|---|
| iPhone 15 pro Max with DJI Osmo Mobile 6 | PolyCam scan settings: lidar object scanning Shutter: Auto Mode | 2 min | Exported .laz file: 21 mb Exported 3D model: 38 mb |
| Nikon D5300 Nikon 35 mm prime objective | Data type: JPG Resolution: 0.8 mm/pix (reprojection error 0.75 pix) White balance: auto Aperture: f/4.5 ISO: auto Shutter time: auto FL: 35 mm | 15 min | Number of photos: 84 Exported 3D model: 10 GB |
| Artec Leo | HD data: no Data framerate: 60 fps Supplementary texture: 8 fps | 2 min | Size of raw data: 9.33 GB Exported 3D model: 1 GB |
| Differential GPS (dGPS): GCX3 Sokkia Bullet GNSS base/rover | N/A | 1 min | N/A |
Table 3
Technical details of processing laptop.
| LAPTOP | SPECIFICS |
|---|---|
| Processor | Intel Core i9-12900HX, 16 cores |
| Graphics Card | GeForce RTX 3080 Ti, 16 GB |
| Processing memory | 32 GB RAM DDR5 |
| Operating System | Windows 11 Pro |
Table 4
Technical details of Agisoft Metashape processing.
| ALIGN | POINT CLOUD | MODEL | TEXTURE | ORTHOMOSAIC |
|---|---|---|---|---|
| Generic preselection, High accuracy, Key point 40k, Tie point 4k | Depth Maps source, High quality, Mild Depth Filtering | Point Cloud source, Arbitrary Surface Type, 200.000 face count, Interpolation enabled | Diffuse Map, Image source data, Default Blending Mode, texture size 8192, enabling Hole Filling and Ghost Filter | Model Surface, Default Blending Mode, enable Hole Filling, Default Pixel Size |
Table 5
Technical details of Artec Studio processing.
| REGISTRATION | ALIGN | FUSION, TEXTURE |
|---|---|---|
| Global registration: collective, Key frame ratio 0.1, Search features within, mm 15, Smart subsampling turned on | If applicable (multiple scans): manual first, then automatic | Smart fusion (sharp) on each scan, and apply texture |
Figure 2
Resulting segmented point clouds, upper: Artec Leo, center: Nikon D5300, lower: iPhone.
Table 6
Comparison of data processing details.
| DEVICE | PROCESSING SOFTWARE | PROCESSING TIMES | GEOREFERENCING | RESULT |
|---|---|---|---|---|
| iPhone | polyCam | 2 min | Targets, DGPS, CloudCompare | Pointcloud: 3,051,240 points Mesh: 157,668 polygons Texture resolution: 4096 × 4096 No. of textures: 1 |
| Nikon D5300 | Agisoft Metashape | 62 min | Targets, DGPS, Metashape | Pointcloud: 88,958,243 points Mesh: 2,799,653 polygons Texture resolution: 8192 × 8192 No. of textures: 1 |
| Artec Leo | Artec Studio | Ca. 30 min | Targets, DGPS, CloudCompare | Pointcloud: 6,011,060 points Mesh: 11,964,597 polygons Texture resolution: 8192 × 8192 No. of textures: 1 |
Figure 3
Comparison of GCP coordinates with locations on the SL mesh, left: target 12 above mesh, middle: target 13 under mesh, right: target 14 just under mesh.
Figure 4
Distance deviation distributions of control points versus the different 3D capture techniques. Left: CTR vs SL cloud, center: CTR vs LiDAR cloud, right: CTR vs PG cloud.
Table 7
Cloud to CTR distances (N = 10).
| METRIC | CTR vs SL CLOUD | CTR vs LIDAR CLOUD | CTR vs PG CLOUD |
|---|---|---|---|
| Mean | 7 mm | 9 mm | 7 mm |
| StDev | 3 mm | 2 mm | 2 mm |
| Max | 14 mm | 14 mm | 13 mm |
Table 8
Cloud to Cloud distances of LiDAR, PG, and SL clouds (N = >1.000.000).
| METRIC | LIDAR vs SL CLOUD | PG vs SL CLOUD | LIDAR vs PG CLOUD |
|---|---|---|---|
| Mean | 1.9 mm | 0.7 mm | 1.4 mm |
| StDev | 1.6 mm | 0.5 mm | 1.2 mm |
| Max | 25 mm | 23 mm | 22 mm |
Figure 5
Distance deviation distributions of different combinations of 3D capture techniques. Left: LiDAR vs SL cloud, center: PG vs SL cloud, right: PG vs SL cloud.
Figure 6
Illustration of the M3C2 cloud distance comparison method (Barnhart & Crosby 2013).
Table 9
M3C2 comparison between LiDAR and SL clouds, cloud to cloud comparison of densified LiDAR and SL clouds (N = >1.000.000).
| METRIC | LIDAR vs SL CLOUD M3C2 | DENSIFIED LIDAR vs SL CLOUD |
|---|---|---|
| Mean | 0.8 mm | 1.9 mm |
| StDev | 1.5 mm | 1.6 mm |
| Max | 3.5 mm | 24 mm |
Figure 7
Assessment of the effect of computation method on cloud to cloud distances. The graphs display the distance distributions of LiDAR vs SL. Left: M3C2 computation method. Right: C2C computation method.
Figure 8
Extracted segment used for volume comparison (PG mesh).
Figure 9
Volume comparisons. Left: SL vs LiDAR cloud, center: SL vs PG cloud, right: PG vs LiDAR cloud.
Table 10
Volume comparison of LiDAR, PG, and SL clouds.
| METRIC | LIDAR vs SL CLOUD | PG vs SL CLOUD | LIDAR vs PG CLOUD |
|---|---|---|---|
| Volume | –0.000 mm3 | –0.000 mm3 | –0.000 mm3 |
| Surface | 0.214 m2 | 0.214 m2 | 0.214 m2 |
| Matching cells | 99.8% | 99.8% | 99.8% |
Figure 10
Texture comparisons. Upper: SL texture, center: PG texture, lower: LiDAR texture.
Figure 11
Texture comparisons. Left: SL texture, center: PG texture, right: LiDAR texture. White dots are CTRs.
Figure 12
Resulting segmented point clouds of SL (upper), PG (middle), and LiDAR (lower) clouds, highlighting the differences in point density and data coverage in hard to reach areas.