# Tracking and Registration Technology Based on Panoramic Cameras
## 4 Experiments
### Complexity Comparison  
Table 7 compares the algorithmic complexity of different models, focusing on the number of parameters and computation time. The test data consists of five randomly selected scenes from the test set, each containing 5000 images with a resolution of 256 \(\times\) 512. For the traditional method, OpenVSLAM, the evaluation was conducted on an Intel Xeon(R) CPU, while for deep learning-based methods, an NVIDIA RTX 3090 GPU was used. The computation time refers to the average inference time across all test data.  
As shown in Table 7, the traditional method, OpenVSLAM, estimates camera poses using handcrafted features and does not involve any learnable parameters, resulting in an inference time of 45.52 ms. 360-SelfNet estimates depth and camera poses based on cubemap panoramic images, with a model size of 33.32 M parameters and an inference time of 28.29 ms. BiFuse++ employs a depth estimation network composed of two ResNet34 encoders and four fusion modules, along with a pose estimation network based on a ResNet18 backbone. This results in a total of 60.88 M parameters and an inference time of 38.11 ms. The method proposed in this chapter designs lightweight sub-networks for both scene depth estimation and camera pose estimation, achieving a total model size of 20.26 M parameters and an inference time of 24.65 ms. Compared with 360-SelfNet and BiFuse++, the proposed method reduces the number of parameters by 39.20% and 66.72%, respectively, and decreases inference time by 12.87% and 35.32%. Therefore, the proposed approach achieves relatively high accuracy with significantly lower model complexity.  
Figure 6 illustrates the impact of image resolution on model performance. In Figure 5(a), the horizontal axis represents the number of pixels in the vertical direction of the equirectan  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Supervision Types** & **RPE-R (°) \(\downarrow\)** & **RPE-T (m) \(\downarrow\)** \\ \hline Fully Unsupervised w/M & 6.88 & 0.023 \\ Fully Unsupervised w/o M & 6.96 & 0.025 \\ Fully Unsupervised w/D\({}_{1}\) & 7.02 & 0.028 \\ Supervised w/Pose labels & 4.43 & 0.017 \\ Supervised w/Noisy Pose labels & 5.75 & 0.021 \\ Supervised w/Pose Estimator Only & 3.47 & 0.013 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation results of panoramic pose estimation. \(\downarrow\) indicates that lower is better.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **OpenVSLAM [13]** & **360-SelfNet [18]** & **BiFuse++ [22]** & **Ours** \\ \hline Parameters (M) & - & 33.32 & 60.88 & **20.26** \\ Computation Time (ms) & 45.52 & 28.29 & 38.11 & **24.65** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance comparison. **Bold** values denote the best performance, and underlined values represent the second-best results.  
gular panoramic image, corresponding to resolutions of 256 \(\times\) 512, 512 \(\times\) 1024, 768 \(\times\) 1536, and 1024 \(\times\) 2048. The vertical axis denotes the number of images processed per second by the model, i.e., the frame rate (Frames Per Second, FPS). In Figure 5(b), the vertical axis represents the ratio of FPS between the proposed method and other approaches. As shown in Figure 5(a), the FPS gap increases with rising image resolution. Figure 5(b) demonstrates that the FPS ratio consistently grows as the resolution increases, indicating that the performance of the proposed method degrades more slowly compared to other methods. Therefore, when performing pose estimation on high-resolution panoramic images, the proposed method can significantly reduce computational cost.