# Pose Tracking and Object Reconstruction Based on Occlusion Relationships in Complex Environments
## 4 Conclusions and Discussion  
With the decreasing cost of 3D data acquisition devices, tasks relying on depth data for object pose estimation and tracking have gained broad prospects for development and industrial applications. However, they also face challenges such as complex and diverse environments, occlusions caused by object clustering, and segmentation difficulties. Our research focuses on the problem of tracking occluded objects and proposes a method based on occlusion relationship analysis, which significantly reduces the negative impact of foreground occlusion on object pose estimation. We propose a pose-tracking framework based on occlusion relationships for object pose-tracking tasks in complex environments. By analyzing the pain points of existing research, which struggle with pose estimation in complex environments due to occlusions and inaccurate segmentation, the concept of dual-layer masks is introduced. Depth information is used to detect changes in occlusion relationships, which are then used to maintain a pool of key frames. The input RGB-D images undergo a series of steps, including segmentation masking, rough pose estimation, and pose graph optimization. Additionally, a neural object field is trained during this process, which assists in the pose graph optimization after convergence. In the experimental section, the proposed method demonstrates accuracy comparable to the BundleSDF method on the HO-3D dataset. Furthermore, through targeted self-made datasets, it is shown that even in scenarios with moving occlusions, frequent interactions, and significant motion blur, the proposed method maintains algorithm robustness and pose estimation accuracy.  
It is important to acknowledge that there are still some limitations and areas for improvement in the current work. Our method focuses on the pose tracking of individual objects. However, when multiple objects are present in an image, the current approach segments individual objects using masks and independently calculates the pose of each object without fully utilizing the correlation information between objects. In real world scenarios, adjacent objects often have certain correlations; they may be located on the same plane or follow a certain logic, such as different objects in the same space-time not overlapping with each other. These correlations can not only help constrain and optimize the pose of each object but also elevate the isolated prediction of individual objects to an understanding of the entire 3D scene.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Sensitivity Coefficient**} \\ \cline{3-8}
**Percentage** & **Absolute** & \multirow{2}{*}{**Threshold**} & \multirow{2}{*}{**0.7**} & \multicolumn{3}{c}{**0.8**} & \multicolumn{3}{c}{**0.9**} \\ \cline{3-8}
**\cline{3-8}  
**Author Contributions:** Conceptualization, X.Z. and Y.Z. ([PERSON]); methodology, Y.Z. ([PERSON]); software, Y.Z. ([PERSON]); validation, Y.Z. ([PERSON]); formal analysis, Y.Z. ([PERSON]); investigation, Y.Z. ([PERSON]); resources, X.Z.; data curation, Y.Z. ([PERSON]); writing--original draft preparation, Y.Z. ([PERSON]); writing--review and editing, X.Z. and Y.Z. ([PERSON]); visualization, Y.Z. ([PERSON]); supervision, X.Z.; project administration, X.Z.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China (U23A20312, 62072366).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data is not publicly due to privacy.  
**Conflicts of Interest:** The authors declare no conflicts of interest.