# Comparative Analysis of LiDAR and Photogrammetry for 3D Crime Scene Reconstruction
## 4 Results and Discussion  
### Model 1: LiDAR with an iPhone and Recon-3D App Results  
During the first scan, which took ten minutes, 1,312,097 point clouds were gathered. The scan, which was done during the daytime, had an overall MAE of 0.029 m. This level of precision indicates that LiDAR technology maintains accuracy in capturing scene dimensions and spatial relationships during longer sessions. In the second scan, which lasted for five minutes during daytime, the gathered number of point clouds significantly increased to 2,111,092 and resulted in an overall MAE of 0.012 m. The improved accuracy is due to the increased density of the collected point clouds. This accuracy effectively captures detailed 3D data for crime scene investigation purposes, especially when timing is critical for forensic investigations, where swift yet accurate results are required. While it may seem logical that a longer scanning duration using LiDAR would gather more point clouds, when implemented, the prolonged scanning (ten minutes) caused a reduced number of registered point clouds, leading to lower mapping accuracy. The mobile Recon-3D app crashed and failed to process the dataset, and the cloud system was utilized for the processing. It seems like the Recon-3D program performs the point cloud registration very conservatively, rejecting many point clouds as outliers, particularly if there is high distance variation between the camera and the surface. The third scan, which took place at nighttime for over ten minutes using the built-in flash of the iPhone, gathered a total of 1,143,808 point clouds. However, this process required a processing time of approximately 9 h and 40 min over the cloud. Despite the low-light conditions, this scan achieved an MAE of 0.021 m, like the first scanned model, which also lasted ten minutes in the daytime. This elevated level of accuracy was achieved in a pitch-dark environment, and using the iPhone flash in this setup shows how the built-in iPhone flash features are beneficial where lighting conditions may not always be ideal.  
The last scan was also from nighttime, but the scanning duration was reduced to five minutes with the iPhone flash on. It generated the highest number of point clouds of 2,374,383, surprisingly achieving an MAE of 0.0066 m, indicating an exceptionally high level of accuracy. This notable increase in point cloud size and the low MAE value suggests that quick scanning with iPhone flash can result in exact and detailed 3D models, which is valuable for documentation and analysis in low-light scenarios. However, it should be  
Figure 3: Measurements of five pairs of markers using CloudCompare.  
noted that for this scan, while performing well regarding the accuracy measurements, the actual scene color and visual representation had a lot of discrepancies across the different areas in the scene; also, shadows and some objects were not aligned properly. Figure 4 shows the texturized LiDAR mapping results, comparing the four scenarios. The result from Scenario 4 shows the most accurate 3D map due to the reduced sunlight interference in the LIDAR sensor, but the visual representation was poor due to the use of a flashlight. Scenario 2 shows the most optimal performance, balancing the map accuracy and visual representation of the scene.