# Diverse Humanoid Robot Pose Estimation from Images Using Only Sparse Datasets
## 5 Results and Evaluation
### Comparison with Other Methods  
In this section, we compare our method with existing approaches for humanoid robot pose estimation. Notably, prior methods [5; 6; 7; 8] are not suited for full-body pose estimation across diverse humanoid robots or other robot types. For baseline comparison, we adopt the state-of-the-art _RoboCup_ method from [9]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & \(mAP^{obj}\) & \(AP^{75}\) & \(AP^{85}\) & \(mAR^{obj}\) & \(AR^{75}\) & \(AR^{85}\) \\ \hline
4-stage hourglass & 84.9 & 93.2 & 82.1 & 75.1 & 80.6 & 64.5 \\
5-stage hourglass & 86.7 & 94.3 & 84.5 & 79.2 & 86.3 & 70.4 \\
6-stage hourglass & 86.4 & 93.9 & 83.9 & 79.8 & 86.9 & 71.7 \\
7-stage hourglass & **87.4** & **95.3** & **86.2** & **81.5** & **88.6** & **74.4** \\
8-stage hourglass & 83.6 & 92.5 & 78.1 & 75.2 & 81.9 & 62.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of models by stage number on the DHRP test set per object. \(mAP^{obj}\) and \(mAR^{obj}\) are used as the evaluation metrics. The best results are shown in bold, and the worst are underlined. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **Nose** & **Neck** & **Sho** & **Elb** & **Wri** & **Hip** & **Knee** & **Ank** & \(mAR^{int}\) \\ \hline
4-stage hourglass & 89.9 & 89.9 & 88.2 & 76.8 & 70.3 & 89.0 & 85.0 & 80.5 & 83.7 \\
5-stage hourglass & 90.6 & 90.9 & 90.3 & 79.0 & 73.4 & 91.3 & 87.5 & 83.9 & 85.9 \\
6-stage hourglass & 89.4 & 90.2 & 89.5 & **80.7** & **75.5** & 90.2 & **88.0** & 84.4 & 86.0 \\
7-stage hourglass & **92.1** & **91.1** & **91.2** & 80.6 & **75.5** & **92.4** & **88.0** & **86.2** & **87.1** \\
8-stage hourglass & 88.7 & 88.4 & 88.7 & 78.0 & 69.1 & 87.7 & 86.4 & 83.0 & 83.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of models by stage number on the DHRP test set per joint. \(mAR^{int}\) is used as the evaluation metric. The best results are shown in bold, and the worst are underlined. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Parameters** & **GFLOPs** & **Training (h)** & **Inference (FPS)** \\ \hline
4-stage hourglass & 13.0 M & 48.66 & 10.5 & 63.64 \\
5-stage hourglass & 16.2 M & 58.76 & 12.8 & 54.56 \\
6-stage hourglass & 19.3 M & 68.88 & 14.6 & 48.34 \\
7-stage hourglass & 22.4 M & 78.98 & 17.7 & 42.81 \\
8-stage hourglass & 25.6 M & 89.10 & 19.8 & 38.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Evaluation of network models by stage number. All models process normalized input images of \(320\times 320\) pixels, obtained by transforming arbitrary image sizes. _RoboCup_ is designed for detecting six end-effector joints in multiple humanoid robots within RoboCup league environments. Its CNN architecture uses a single-stage encoder-decoder model with ResNet18 as the backbone. We trained this model on our DHRP dataset following the approach outlined in [9]. Details of the network architectures are provided in Table 13.  
Comparative evaluations on the DHRP test set are presented in Table 14. As the single-stage _RoboCup_ model was originally designed for a smaller dataset (1.5k) focused on RoboCup robots, its performance is inadequate when applied to our larger and more diverse DHRP dataset (15k images, 14 joints, multiple robot types, diverse motions). In particular, _RoboCup_ struggles with accurate limb joint detection. In contrast, our deeper, multi-stage network shows significant accuracy improvements across the board. We also compare our pose estimation network using an external dataset provided by the digital human-augmented robotic telepresence method [10], which represents the state-of-the-art technique in full-body 3D pose estimation for a single humanoid robot type. This approach estimates 3D poses for a specific humanoid robot using 2D keypoint detectors from head-worn stereo cameras.