# Add new tracks that are only present in the new frames of GraphB
## 3 Results and Discussion  
The proposed method in this study was tested using three image datasets, and the reconstruction results were compared with the original depth map. All images were captured using a DSLR camera from different angles. Following photogrammetry and stereo photography techniques, the camera was rotated 15-20 degrees for each shot. Three images were used in the experiments, each divided into 21, 9, and 18 sub-images, respectively. All images were JPEG-compressed and had a uniform resolution of 12 megapixels. The test results, including depth information accuracy, running time, and number of generated point clouds, are summarized in Table 2. The comparison of reconstruction results between the proposed method and the original depth map is shown in Table 3. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Image Dataset \#2**} \\ \hline
**Input** & **Proposed** & **Depth** & **SfM**[19] & **MVS**[22] \\
**Image** & **Method** & **Map** & **SfM**[19] & **MVS**[22] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._The experiment demonstrates that the proposed method provided depth information accuracy exceeding 65% across all tested image datasets. In Dataset 1, the proposed method achieved 70.7% accuracy, closely approaching the SfM method's 72.2%. Both significantly outperformed the depth map and MVS methods. The Dataset 2 results further confirmed the superiority of the proposed method, which achieved 79% accuracy, surpassing all other methods. The depth map method performed the worst (20.4% accuracy), while SfM (71.7%) and MVS (69.9%) trained behind the proposed method. For Dataset 3, the results were closer, with the proposed method achieving 67.3% accuracy, compared to 65.5% (SfM) and 66.2% (MVS). Again, the proposed method demonstrated the highest accuracy, while the depth map method remained the least accurate at 25.5%. The depth map method also demonstrated a clear advantage in terms of running time. It consistently completed in under 1 s across all three image datasets. In contrast, the proposed method required 120 s for Dataset 1 (21 images), 72 s for Dataset 2 (9 images), and 96 s for Dataset 3 (18 images). Both the SfM and MVS methods exhibited even longer running times than the proposed method across all datasets: SfM required 156, 98, and 128 s, while MVS took 220, 160, and 184 s, respectively. This experiment demonstrates that, even with consistent image size and JPG format, different methods yield varying results for depth information accuracy and running time. While the number of images used influences running time, it does not appear to significantly impact the accuracy of depth information. Importantly, the proposed method stands out by achieving the highest depth information accuracy (79%) for Image Dataset 2. This may be related to the distinct color contrast between objects and backgrounds within those images, a characteristic less prominent in the other datasets. The proposed method demonstrated superiority in generating point clouds. It consistently yielded the highest count across all three image datasets (978,490, 2,588,931, and 2,759,780 positions, respectively). The depth map method was consistently placed second (725,830, 814,300, and 943,360 positions). Importantly, the results indicate that image size does not significantly influence the number of point clouds generated. Recent works based on depth maps [44; 45], SfM [46; 47], and MVS [48; 49] were also investigated. In [44], a novel approach to real-time dense visual SLAM (Simultaneous Localization and Mapping) was presented. This system is capable of capturing comprehensive, dense, and globally consistent surfel-based maps of room-scale environments. It operates incrementally in an online fashion using an RGB-D camera without relying on pose graph optimization or any post-processing steps. This was accomplished using dense frame-to-model camera tracking and windowed surfel-based fusion coupled with frequent