# Comparative Analysis of LiDAR and Photogrammetry for 3D Crime Scene Reconstruction
## 4 Results and Discussion
### Comparison of Model 1 and Model 2  
Figure 6 presents the comparative analysis of Model 1 (LiDAR) and Model 2 (photogrammetry) using the MAE values of marker pairs for four scanning scenarios. The data indicate that Model 1 consistently demonstrates lower MAE values than Model 2, affirming its accuracy and consistency. Interestingly, Scenario 1's scan (10 min, daytime) of the photogrammetry model showed better accuracy than the LiDAR model, which is because the photogrammetry model registered more point clouds, while the LiDAR sensor's prolonged scanning resulted in rejecting data and a reduction in the model's point clouds. Another reason may be the extensive sunlight interference in LiDAR sensing in daytime. This confirmed that the LiDAR map from Scenario 4 (5 min, nighttime) showed the best accuracy due to the lowest sunlight interference at nighttime. However, the textured LiDAR map was visually poor due to the poor image quality. Therefore, Model 1 from Scenario 2 was chosen for a point-wise comparison with the Leica BLK360 G2 reference model. Regarding the third and fourth scans, Model 1 maintained a lower MAE across marker pairs than Model 2, indicating that the LiDAR model performed better in pitch-dark settings by relying on the iPhone's built-in flash. Figure 5: Three-dimensional map results of Model 2 using photogrammetry with four scanning scenarios, showing the best results in daytime. The LiDAR model generally achieved lower overall MAE values, with the differences between the scans becoming more pronounced in reduced-light conditions. In such scenarios, photogrammetry scans struggled to maintain accuracy, leading to an increase in the overall MAE. Conversely, the LiDAR model's overall MAE clearly showed that the prolonged 10 min scanning time, whether in the daytime (as demonstrated in Scenario 1) or at night (as shown in Scenario 3), results in decreased accuracy compared to the shorter-duration scans of 5 min (as shown in Scenarios 2 and 4) due to the previously mentioned explanation of the Recon-3D point cloud registration problem. From our analysis of LiDAR (Model 1) and photogrammetry (Model 2) in different scanning scenarios, the second LiDAR scan of Model 1 was chosen as the best-scanned model since it achieved a balance between accuracy and visual quality. This scan, which took five minutes to complete during the daytime, captured double the number of point clouds compared to the longer ten-minute duration of the first LiDAR scan of Model 1. The higher point density increased accuracy and showed good visual quality since it was conducted in the daytime with natural lighting. In contrast to the third and fourth LiDAR scans of Model 1 that were performed at night, they had issues like ghosting, missing parts, and color inaccuracies. The chosen scan offered a precise and visually appealing representation of the mock-up crime scene. On the other hand, when comparing the chosen second LiDAR scan of Model 1 with the most accurate scan of the photogrammetry model (Model 2), the first scan lasted for ten minutes, doubled the amount of scanning time, and had a higher MAE of 0.0166 compared to 0.012. Moreover, the scene had various missing parts, though it produced a denser point cloud, as shown in Figure 5a. The chosen model (second LiDAR scan of Model 1) was compared to the reference model (Leica BLK 360 G2 Model 3). First, the ICP algorithm was applied to align two point clouds, which is required for model comparisons [34]. Figure 7 shows a heatmap of cloud-to-cloud distance errors, presenting a more detailed map. Based on the heatmap histogram, around 90% of the point clouds have an absolute distance of 0.1224 m between them. This indicates the distance difference and how close the point clouds are from the aligned model of the iPhone's LiDAR from the professional-grade Leica BLK360 scanner.