# Pose Tracking and Object Reconstruction Based on Occlusion Relationships in Complex Environments
## 3 Experiments
### Comparison and Analysis
#### 3.5.1 Comparative Experiments on Moving Occluders  
To verify the effectiveness of the occlusion detection algorithm proposed in our research, this section conducts a comparative experiment. The experiment contrasts our method with the state-of-the-art method in pose tracking algorithms--BundleSDF. The experimental setup involves a stationary tracking target (red box) relative to the camera, while a white box is consistently present in the scene, positioned between the camera and the target object, serving as an occluder. Additionally, this occluder intermittently moves, altering the occlusion relationship between the two objects. It is important to note that throughout the entire recording process, the target object (red box) remains occluded, with no frame showing the front of the target object fully exposed in the scene. The experimental results are obtained under the optimal parameter settings and represent the final output of the optimization process, as shown in Figure 6. The left side visualizes the tracking results (yellow boxes represent the target object boundary boxes predicted by the method), while the right side displays rendered views of the  
Figure 5: Visualizations of partial results of our method on the self-made indoor interaction dataset are presented. Each row represents the results of one video in the dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Reconstruction CD (cm)**} & \\  & **BundleSDF** & **Ours** \\ \hline _firm\_cushion_ & 2.13 & **0.75** \\ _toy_ & 1.67 & **0.31** \\ _box_ & 1.75 & **0.52** \\ _airpods_ & 2.60 & **2.56** \\ _switch_ & 1.58 & **0.21** \\ _cup_ & 1.66 & **0.47** \\ mean & 1.90 & **0.80** \\ \hline \hline \end{tabular} The symbol \(\downarrow\) indicates that lower values represent reconstruction results that are closer to the ground truth. **Bold** values represent the best performance.  
\end{table} 
Table 3: Comparison of reconstruction accuracy on the self-made dataset. reconstructed models from two angles. BundleSDF and our method's results are arranged in corresponding pairs. As the tracking progresses, the occluder (white box) transitions from a vertical to a horizontal orientation and undergoes random lateral movements. Firstly, in terms of discerning the motion of the target object, BundleSDF is notably influenced by the foreground object (white box) in the third image, resulting in a significant deviation of the boundary box as the foreground object moves to the left of the frame. In contrast, our method maintains overall stability. Additionally, throughout the tracking process, there are significant differences in the positions of the boundary boxes drawn by the two methods. Specifically, the boundary boxes drawn by BundleSDF are not parallel to the plane where the target object resides in the scene; instead, they exhibit a certain degree of tilt and fail to accurately reflect the pose of the target object. In contrast, our method consistently maintains boundary boxes tightly around the target object throughout the tracking process, unaffected by the movement of the occluder. From the reconstruction results, since the target object (red box) only appears frontally in the video data, ideally, the reconstruction result should be a rectangular plane. When viewed from the front, BundleSDF's reconstruction result outlines a rectangular plane overall, but there is a hole in the middle of the rectangle, occupying approximately 20% of the entire front face, and the shape of the hole is rectangular. Due to the occlusion by the white box, the area where the hole is located is not captured by the camera for a considerable period. Additionally, due to the factor mentioned earlier (segmentation leading to unclear geometric boundaries), the method mistakenly considers the boundary of the occluder as the geometric boundary of the target object, resulting in the formation of the hole.