# LiDAR as a Geometric Prior: Enhancing Camera Pose Tracking Through High-Fidelity View Synthesis
## 5 Experiments
### Experiments with Online Rendering and Tracking  
This experiment analyzes the Online Rendering and Tracking method, as explained in Section 3. Each sequence has been processed using both the AKAZE and ORB descriptors, measuring the ATE (Absolute Trajectory Error) and the total number of tracked frames, as in previous works [5]. Table 1 presents the performance of our online tracking method using two different keypoint detectors: AKAZE and ORB. The comparison, based on the Absolute Trajectory Error (ATE) for accuracy and the percentage of tracked frames (Trck) for robustness, shows that AKAZE consistently outperforms ORB across all nine datasets. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**AKAZE**} & \multicolumn{2}{c}{**ORB**} \\  & **ATE (m)** & **Trck (\%)** & **ATE (m)** & **Trck (\%)** \\ \hline Garden & 0.031 & 84.1 & 0.045 & 81.3 \\ RetailStreet & 0.037 & 92.8 & 0.056 & 92.8 \\ CBDBuilding & 0.048 & 93.5 & 0.091 & 79.0 \\ HITGraffitiWall & 0.050 & 94.8 & 0.069 & 94.7 \\ HKULandmark & 0.055 & 83.6 & 0.083 & 80.2 \\ Drive & 0.070 & 93.2 & 0.132 & 89.2 \\ HKUCulturalCenter & 0.086 & 90.7 & 0.163 & 78.6 \\ RedSculpture & 0.107 & 90.5 & 0.210 & 72.9 \\ SYSU & 0.103 & 78.4 & 0.122 & 75.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Tracking results of the online rendering and tracking approach, comparing AKAZE and ORB detectors. ATE values are rounded to three decimal places. On average, AKAZE reduces the ATE by approximately 37% compared to ORB. This accuracy improvement is particularly significant in challenging sequences, with the error being reduced by nearly 49% in the _RedSculpture_ dataset (0.107 m vs. 0.210 m) and by 47% in the _CBDBuilding_ dataset (0.048 m vs. 0.091 m). In terms of robustness, AKAZE also demonstrates a clear advantage, consistently tracking a higher percentage of frames. The improvement is most pronounced in the _HKUCLulturalCenter_ sequence, where AKAZE successfully tracks over 12% more of the trajectory than ORB. Even in the one dataset where both detectors achieve an identical tracking rate, _RetailStreet_ (92.8%), AKAZE remains significantly more accurate. These results strongly suggest that the features provided by AKAZE are more stable and discriminative for matching between real and rendered images. Consequently, AKAZE is the more suitable choice for our tracking pipeline, especially in real-world scenarios with substantial variations in viewpoint or texture. The architecture is designed for maximum responsiveness. The rendering and tracking processes run in parallel and are decoupled. The tracking thread operates at its maximum speed, using the most recently completed synthetic image as its reference without being blocked by the rendering process. This results in a tracking loop latency determined only by the keypoint stage: approximately 33.3 ms (30 FPS) for AKAZE and 12.5 ms (80 FPS) for ORB. The rendering thread updates the reference view in the background at its own pace of 16 FPS. This design ensures the system is highly responsive and can comfortably process a 30 Hz camera feed in real-time. While this introduces a minor, acceptable delay in the _freshness_ of the rendered view, the tracking itself remains fast. The Impact of IMU Data  
To assess the contribution of the Inertial Measurement Unit (IMU) to our Online Rendering and Tracking pipeline, we conducted an ablation study where the IMU-based pose prediction step was entirely removed. In this configuration, the pose estimated from the previous frame serves as the sole prior for the current frame. This experiment allows us to isolate the performance of the vision-only tracking component and quantify the benefits of incorporating inertial data. The results of this experiment are presented in Table 2.  
A comparison between these results and those in Table 1 reveals the influence of the IMU on tracking performance. For the AKAZE detector, removing the IMU results in a minor degradation in performance.