A Comparative Study of Mobile 3D Reconstruction Workflows for Crash-Damaged Vehicle Documentation

The three-dimensional documentation of crash-damaged vehicles can support the visual and geometric recording of deformation, but it is unclear how complete mobile reconstruction workflows compare when applied to the same vehicles. This study compared three workflows using a single consumer device, an Apple iPhone 16 Pro Max: reconstruction from photographs, reconstruction from extracted video frames, and direct mobile light detection and ranging (LiDAR) scanning. Three damaged vehicles were documented: a Volkswagen Passat B6 Variant, a Toyota Auris, and a Toyota Yaris. RealityScan was used for reconstruction from photographs and video frames, and Polycam was used for the LiDAR scans. In CloudCompare, all models were cleaned, scaled using the known wheelbase, registered to the LiDAR reference by the Iterative Closest Point algorithm, and compared using cloud-to-mesh distances, with the principal quantitative statistics based on absolute point-to-surface distance magnitudes. Because the mobile LiDAR model served as an internal reference rather than as an independent metrological ground truth, the reported values describe residual post-registration point-to-surface deviations and not absolute geometric accuracy. The principal surface evaluation used exactly 100,000 surface-sampled points per evaluated direction and bidirectional cloud-to-mesh calculations. The standardised results did not show a uniform ordering between reconstruction from photographs and reconstruction from video frames. In the reconstruction-to-LiDAR direction, median absolute distances ranged from 0.03082 to 0.03677 m for the Passat, from 0.02900 to 0.03413 m for the Auris, and from 0.05953 to 0.06168 m for the Yaris. Lower reverse-direction median values and the broader upper-tail distributions observed for the Yaris demonstrated the directional character of the surface comparison. The Yaris showed larger, long-tailed deviations concentrated mainly in the rear and left-lateral damaged regions. However, because each damage configuration was represented by only one vehicle, the observed differences cannot be attributed to damage type alone. The three workflows provided complementary geometric and visual information for crash-damaged vehicle documentation, although model fusion and accident-reconstruction parameters were not evaluated in this study. Because only one acquisition was performed for each vehicle–workflow combination, the findings should be interpreted as an exploratory comparison rather than as an assessment of repeatability, operator variability, or measurement uncertainty.