# Pose Tracking and Object Reconstruction Based on Occlusion Relationships in Complex Environments
## 3 Experiments
### Comparison and Analysis
#### 3.5.2 Frequent Interaction Comparative Experiment
The first row depicts the reconstruction results by BundleSDF, while the second row shows the reconstruction results by our method. From left to right, the observations are from the frontal, side, and top views of the object, respectively. Figure 7: The partial pose tracking results for the _video_switch_ dataset are shown below: yellow wireframes represent the pose estimation results of the method. (**a**) The results of the comparative method, BundleSDF; (**b**) the results of our method. 2. Throughout the entire process, the target object is never fully exposed in the frame (even when taken out from behind the occluding object, there are also fingers blocking). 3. The video includes rapid movements of the target object being quickly taken out from behind the occluding object (the switch gaming machine) and promptly placed back. Additionally, there is camera movement present. While camera movement also pertains to the relative pose changes discussed in this paper, camera movement more often leads to image blur, as depicted in Figure 9.  
The tracking and reconstruction results are shown in Figure 10. The evaluation of the pose tracking result is based on the proximity and alignment of the bounding boxes. Firstly, for the tracking results obtained with BundleSDF, there is a noticeable error in estimating the object size, as the bounding box only encompasses the upper half of the cup. Combined with the reconstruction results, it can be inferred that this error occurred when the cup was taken out from behind the occluder, and the method failed to capture the lower half of the cup in time, resulting in an incorrect judgment of the overall shape of the target object. Our method overcomes this difficulty well, even though the lower half of the cup only briefly appears. The change in occlusion allows the method to perceive the existence of more parts of the target object in a timely manner, indicating the need to add key frames to improve the completeness of the reconstruction. This conclusion is corroborated by the reconstruction results: the overall contour of the cup reconstructed by our method conforms to the tall and slender cup shape, and the notch appearing in the middle is due to occlusion by the fingers during the handheld process. When the camera is in motion, such as due to high speed or shaking, causing image blurring, it is an occasional event. However, it is often in such situations that object tracking methods are most needed to provide stable and effective pose estimation results. In Figure 9, three frames with significant motion are selected to compare the robustness of the two methods under these conditions. The first column on the left shows the tracking results of BundleSDF, and the second column shows the local magnification of each frame. From the images, it can be observed that all three frames have varying degrees of blurring. The combination of intense motion and blurry images directly leads to incorrect pose estimation by BundleSDF, and even complete deviation, resulting in tracking failure. Under these conditions, our method also faces significant challenges. In the first and third  
Figure 9: The partial results of the tracking for _video_cup_ are shown above. The left two columns display the original images and local enlargements of the pose estimation results by BundleSDF, while the right two columns show the original images and local enlargements of the pose estimation results by our method. frames, the predicted pose deviates to the left, and in the second frame, there is an incorrect judgment of the target's orientation. Overall, although there is some fluctuation, the bounding boxes still surround the target object and form an enclosure around it, indicating that the method still provides relatively reasonable prediction results under these conditions.