# Pose Tracking and Object Reconstruction Based on Occlusion Relationships in Complex Environments
## 3 Experiments
### Results and Analysis  
#### 3.4.1 HO-3D Dataset  
This section continues to visualize the pose estimation results by converting them into object bounding boxes in the images. As shown in Figure 4, the red bounding boxes represent the predicted results of our method, while the green bounding boxes represent the annotated ground truth poses. By comparing the similarity between the two, the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter Category** & **Parameter Name** & **Parameter Size** \\ \hline \multirow{2}{*}{Loss Term} & \(\omega_{f}\) & 1.0 \\  & \(\omega_{p}\) & 1.0 \\  & \(\omega_{s}\) & 1.0 \\ \hline \multirow{4}{*}{Hyperparameters} & Point Cloud Visibility Threshold & 0.1 \\  & Sensitivity Coefficient & 0.8 \\ \cline{1-1}  & Ratio Threshold & 0.05 \\ \cline{1-1}  & Absolute Threshold & 500 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Key parameter setups. effectiveness of the pose estimation can be directly observed. In terms of qualitative analysis, the projected bounding boxes of our method's estimation results closely match the ground truth bounding boxes, indicating a high degree of overlap. This suggests that the predicted results of our method accurately reflect the true pose of the target object in the current frame, providing effective pose information. The results of estimating each object in the dataset are presented in this section, with efforts made to select results with different angles and significant motion between frames in chronological order. However, due to space constraints, the section divides the results into time segments, extracting results from different time intervals rather than focusing on a specific time period to ensure a comprehensive display of the entire tracking process. From the results, it is evident that our method accurately predicts the current pose of the target object regardless of its orientation. Furthermore, there is no apparent decrease in tracking accuracy over time, indicating that our method overcomes the drift problem commonly encountered in long-term tracking. Particularly noteworthy are frames from video SM1 (Figure 3(a)) and video MPM14 (Figure 3(b)), where a large portion of the object's surface is occluded by interacting objects (human hands), yet our method still makes correct predictions. This is because, at this point, the algorithm has detected the presence of foreground occlusion, and the key-frame addition strategy becomes more proactive in evaluating each frame. Additionally, it continuously monitors changes in foreground occlusion, responding to sudden decreases in the visible portion of the object's surface. When such situations occur, they leverage information from key frames in similar viewpoints to assist pose optimization, as there is usually more usable texture in similar viewpoints. Moreover, thanks to the modeling of the target's geometric shape during tracking, even when tracking objects with weak texture or smooth surfaces, such as AP12 (Figure 3(c)) and SB11 (Figure 3(d)), which have uniform colors and smooth surfaces, making  
Figure 4: The partial results of our method on the HO-3D dataset are visualized above. Each row represents the results of a video in the dataset, where the green bounding box indicates the ground truth pose, and the red bounding box represents the predicted results of our method. textureless areas extensive and unfavorable for key-point matching, our method still maintains stable tracking under these circumstances. In terms of quantitative evaluation, this study employs ADD, ADD-S [12], and Chamfer distance (CD) as evaluation indices, tabulating the results of target pose estimation for all frames in the HO-3D dataset, as shown in Table 2. Our method achieves 92.56% and 96.51% on the ADD and ADD-S metrics, respectively, with a CD of only 0.50. Performance across all three metrics surpasses BundleSDF and significantly exceeds NICE-SLAM and SDF-2-SDF.