# Tracking and Registration Technology Based on Panoramic Cameras
## 4 Experiments
### Ablation Study  
To investigate the impact of different types of supervision signals on pose estimation performance, ablation studies were conducted on the pose estimation sub-network using supervised learning strategies. The specific ablation experiments are as follows:  
**Fully Unsupervised Training**: The network was trained without any labels by leveraging spherical photometric consistency loss to estimate camera motion.  
**Fully Unsupervised Training without M**: To investigate the role of occlusion handling in the pose estimation process, we remove the occlusion mask module from the pose sub-network. The model is still trained under the same fully unsupervised setting, using only the spherical photometric consistency loss for supervision.  
**Fully Unsupervised Training without D\({}_{1}\)**: To assess the contribution of multi-scale depth features, we remove the low-resolution depth branch and retain only the full-resolution depth map for supervision. The network remains fully unsupervised and continues to rely solely on the spherical photometric consistency loss.  
**Supervised with Pose Labels**: To enhance the supervision signals, pose labels were introduced as supervision, and an L1 loss was used to guide training. This strategy enables the network to better learn pose information, thereby improving the accuracy of pose estimation.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Approaches**} & \multicolumn{4}{c}{**Error Metric \(\downarrow\)**} & \multicolumn{4}{c}{**Accuracy Metric \(\uparrow\)**} \\ \cline{2-7}  & **MRE** & **MAE** & **RMSE** & **RMSElog** & **\textless{} 1.25** & **\textless{} 1.25\({}^{2}\)** & **\textless{} 1.25\({}^{3}\)** \\ \hline
360-SelfNet [18] & 0.1521 & 0.2344 & 0.5121 & 0.0934 & 0.8479 & 0.9420 & 0.9726 \\ BiFuse++ [22] & **0.1176** & **0.1815** & **0.4321** & **0.0790** & **0.8974** & **0.9546** & **0.9773** \\ Ours & 0.1330 & 0.2154 & 0.4730 & 0.0887 & 0.8788 & 0.9480 & 0.9768 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative comparison of panoramic depth estimation results across different methods. \(\downarrow\) indicates that lower is better; \(\uparrow\) indicates that higher is better. **Bold** values denote the best performance, and underlined values represent the second-best results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Approaches** & **RPE-R (\({}^{\circ}\)) \(\downarrow\)** & **RPE-T(m) \(\downarrow\)** \\ \hline OpenVSLAM [13] & **5.36** & **0.018** \\
360-SelfNet [18] & 6.98 & 0.025 \\ Bifuse++ [22] & 6.75 & 0.022 \\ Ours & 6.88 & 0.023 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Quantitative comparison of panoramic pose estimation results across different methods. \(\downarrow\) indicates that lower is better. **Bold** values denote the best performance, and underlined values represent the second-best results.  
**Noisy Pose Ground-Truth Supervision**: Considering that pose estimation in real-world scenarios may be prone to drift, pose noise was simulated by adding Gaussian noise with a mean of 0 and a standard deviation of 0.1 to the ground-truth pose. This mimics the accumulation of pose estimation errors in real-world applications. To mitigate this drift effect, pose labels and the L1 loss were also used for supervised training, allowing the network to gradually reduce pose deviation errors.  
**Pose Estimator Only Training**: The pose estimation sub-network was trained independently, supervised by calculating the L1 loss between the estimated poses and the ground-truth labels.  
As shown in Table 6, training the network with pose ground-truth significantly improves the accuracy of the pose estimation.