# LiDAR as a Geometric Prior: Enhancing Camera Pose Tracking Through High-Fidelity View Synthesis
## 5 Experiments
### Experiments with Offline Keypoint-Map Tracking
#### 5.3.2 M2 Mapping Rendering  
Table 4 presents the results of the enhanced version of the Offline method, which incorporates a refined rendering approach M2 Mapping [2]. This modification aims to improve keypoint projection and stability. As with the previous versions, we evaluate performance using AKAZE and ORB across nine datasets, reporting both the Absolute Trajectory Error (ATE) and the tracking percentage (Trck).  
AKAZE maintains strong performance with M2 Mapping, achieving low ATEs (ranging from 0.025 to 0.103) and high tracking percentages in most cases, notably surpassing 90% in five datasets. The improvements in stability and accuracy are particularly evident in sequences like _CBDBuilding_ (ATE: 0.054 m, Trck: 96.8%) and _RetailStreet_ (ATE: 0.037 m, Trck: 92.8%), demonstrating a slight improvement over the previous projection method.  
In contrast, ORB continues to struggle with robustness. While it achieves low ATEs in isolated cases (e.g., _Garden_, ATE: 0.011 m; _RedSculpture_, ATE: 0.003 m), these results are misleading due to extremely low tracking percentages (1.1% and 0.3%, respectively). In general, ORB's tracking drops dramatically across all datasets, with values below 5% in six out of nine sequences. The only exceptions where ORB approaches usable performance are _RetailStreet_ (90.3%) and _Drive_ (91.1%), but even in those, ATE remains higher than AKAZE.  
Compared to point-based rendering, the M2 Mapping enhancement improves AKAZE's tracking robustness slightly (e.g., +1.3% in _CBDBuilding_, +0.6% in _Drive_) while maintaining a low error. However, it does not resolve the weaknesses of ORB, whose performance remains unstable and highly scene-dependent.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Dataset** & \multicolumn{2}{c}{**AKAZE**} & \multicolumn{2}{c}{**ORB**} \\  & **ATE (m)** & **Trck (\%)** & **ATE (m)** & **Trck (\%)** \\ \hline Garden & 0.039 & 84.1 & 0.011 & 1.1 \\ RetailStreet & 0.037 & 92.8 & 0.061 & 90.3 \\ CBDBuilding & 0.054 & 96.8 & 0.166 & 2.2 \\ HITGrafiffiWall & 0.025 & 94.8 & 0.165 & 4.9 \\ HKULandmark & 0.035 & 86.2 & 0.074 & 1.9 \\ Drive & 0.056 & 93.2 & 0.073 & 91.1 \\ HKUCulturalCenter & 0.063 & 93.8 & 0.357 & 1.2 \\ RedSculpture & 0.103 & 90.4 & 0.003 & 0.3 \\ SYSU & 0.086 & 75.3 & 0.110 & 26.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the Offline Keypoint-Map Tracking method using M2 Mapping rendering. ATE values are rounded to three decimal places.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Dataset** & \multicolumn{2}{c}{**AKAZE**} & \multicolumn{2}{c}{**ORB**} \\  & **ATE (m)** & **Trck (\%)** & **ATE (m)** & **Trck (\%)** \\ \hline Garden & 0.040 & 84.1 & 0.310 & 38.3 \\ RetailStreet & 0.037 & 92.8 & 0.082 & 89.3 \\ CBDBuilding & 0.056 & 95.5 & 0.305 & 0.6 \\ HITGrafiffiWall & 0.035 & 94.8 & 0.377 & 2.0 \\ HKULandmark & 0.056 & 83.8 & 0.222 & 58.9 \\ Drive & 0.088 & 93.1 & 0.001 & 0.3 \\ HKUCulturalCenter & 0.076 & 92.2 & 0.000 & 0.0 \\ RedSculpture & 0.108 & 85.0 & 0.218 & 0.6 \\ SYSU & 0.094 & 81.6 & 0.217 & 11.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Tracking results of the Offline Keypoint-Map Tracking method using point-based rendering. ATE values are rounded to three decimal places.  
These results further support the conclusion that descriptor choice is more critical than rendering refinement. While M2 Mapping enhances projection accuracy, reliable tracking is only achievable when paired with robust and discriminative descriptors such as AKAZE.