# Depth Segmentation Approach for Egocentric 3D Human Pose Estimation with a Fisheye Camera
## 5 Results
### Ablation Study
#### 5.3.2 Segmentation Strategy  
We also evaluated the effects of different segmentation strategies on the performance of the SegDepth module through both qualitative and quantitative analyses. We compared a single-body segmentation approach, which represents the entire body as a single region, with our proposed three-region segmentation, which divides the body into upper body, lower body, and background regions. To further quantify the impact of segmentation strategies on robustness, we evaluated four conditions: The first condition, heatmap only, uses only heatmap features for 3D pose estimation, without incorporating any depth or segmentation information. The second condition, heatmap + depth map (no segmentation), integrates depth map features into the pipeline but does not use any segmentation data. The third condition,  
Figure 5: The qualitative evaluation of our model, Mo2 Cap2, and xR-EgoPose with and without the SegDepth module. The blue skeleton denotes ground truth and red denotes the estimated result.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Approach** & **MPJPE** & **Network Size (Total)** & **Size Reduction** \\ \hline Dual Decoder & 66.4 & 277.8 MB & - \\ Single Decoder & 65.7 & 251.2 MB & 9.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The MPJPE and network size with a single decoder and dual decoder for regressing the segmentation and depth map in the SegDepth module.  
heatmap + depth map + single-body segmentation, combines depth map features with a single-body segmentation approach that considers the entire body as a single region. Finally, the fourth condition, heatmap + depth map + three-region segmentation, combines depth map features with our proposed three-region segmentation strategy for detailed feature extraction. Quantitative results, presented in Table 4, show that the three-region segmentation strategy achieves the best performance with an MPJPE of 65.7. While the integration of depth map features alone reduces MPJPE compared to the heatmap-only condition (from 71.6 to 68.2), the single-body segmentation approach increases MPJPE to 75.8, likely due to the lack of spatial granularity. In contrast, the three-region segmentation strategy further reduces MPJPE to 65.7, demonstrating its effectiveness in improving the accuracy of 3D pose estimation. Qualitative results, as illustrated in Figure 6, show that the three-region segmentation strategy is effective in reducing noise in egocentric fisheye images. Additionally, Figure 7 presents the optimization curves for 3D joint training, highlighting the robustness and accuracy of the proposed method. The approach provides detailed and localized information, improving the network's ability to detect overlapping or occluded body parts.  
Figure 6: Comparison between the single-body segmentation and our three-region segmentation methods. Results show that the single-body segmentation method does not clearly separate the body region, which leads to noise in the depth map and segmentation estimation (red box), while our segmentation shows a fine-grained body silhouette.  
Figure 7: Optimization curves for 3D joint training as a function of epoch. Gray (triangle): heatmap, orange (square): heatmap W depth map, blue (rhombus): heatmap W depth map and single-body segmentation, yellow (cross, our method): heatmap W depth map and three-region segmentation. The optimization curve regressing the three-region segmentation and depth map shows the lowest error while maintaining robustness.