## 5 Results and Discussions
### Comparative Analysis of Fully Visible Objects  
Figure 20 illustrates the average time required to perform depth extraction for fully visible objects at occlusion level 0. Among all approaches, the bounding box and the segmentation mask in CeRDE show the fastest processing times, all under 3 milliseconds. These are significantly faster than the traditional PbyP approach, which ranges from 6 to 16 milliseconds. Notably, the CoRDE approach is the slowest, especially for larger objects like buses and trucks, taking over 200 milliseconds in some cases. The GCRDE approach strikes a balance, offering faster performance than CoRDE but slower than CeRDE. These results indicate that CeRDE is the most time-efficient, making it well-suited for real-time applications.  
Figure 21 presents the accuracy of each method in estimating depth for various object types. The segmentation mask for the CeRDE and GCRDE approaches stands out with the highest accuracy, above 88% for all objects and reaching nearly 97% for \"Person\". Similarly, the bounding box for CeRDE and GCRDE also demonstrates strong performance, although slightly below their segmentation masks' accuracies. In contrast, the CoRDE approach shows the lowest accuracy, particularly for smaller objects like bicycles. PbyP approaches perform reasonably well but lack the consistency seen in CeRDE and GCRDE. These results confirm that central and grid-based region strategies, especially with segmentation masks, significantly enhance depth estimation accuracy.  
Figure 22 shows the RMSE values for depth estimation, where lower values indicate higher precision. The CeRDE approach again outperforms all others, with the segmentation mask and the bounding box achieving the lowest RMSEs, particularly for large objects like buses and trucks. The bounding box method in the GCRDE approach provides moderate error values. PbyP and CoRDE approaches show significantly higher RMSEs, especially for buses, where values exceed 6 m in some cases. These findings show that CeRDE delivers the most precise depth estimations.  
Figure 21: This comparison shows depth extraction accuracy (%) at occlusion Levels 0 for the traditional approach: PbyP (Point-by-Point) and our proposed approaches: CoRDE (Complete Region Depth Extraction), CeRDE (Central Region Depth Extraction), and GCRDE (Grid Central Region Depth Extraction).  
Figure 20: This comparison shows mean depth extraction time (in milliseconds) at occlusion Levels 0 for the traditional approach: PbyP (Point-by-Point) and our proposed approaches: CoRDE (Complete Region Depth Extraction), CeRDE (Central Region Depth Extraction), and GCRDE (Grid Central Region Depth Extraction).  
The comparative evaluation of depth extraction methods under occlusion level 0 demonstrates that CeRDE, especially when combined with segmentation masks, offers the best performance overall. While the GCRDE approach provides a good compromise between speed and accuracy, it falls short of matching CeRDE's optimal results. The CoRDE approach is both slow and less accurate, indicating that a more extensive Complete Region Depth Extraction processing may introduce noise.