# Selection of Key Frames for 3D Reconstruction in Real Time
## 4 Results and Discussion
### Reconstruction  
For 3D reconstruction, we used the open-source photogrammetry software Meshroom [23] to show the results of the proposed method in a simple way. We started two experiments, as shown in Figure 10. First, we performed a reconstruction with all images, as well as with the selected key frames of a video recording. Meshroom is modular and therefore highly customizable. For a simple demonstration of the differences, we set the default settings. The reconstruction was performed on a system equipped with a NVIDIA RTX3090, 64 GB RAM, and Intel Core i7. Table 2 below shows the result information of both reconstructions. The root-mean-square error (RMSE) relates to the _StructureFromMotion_ node and describes the deviation of the projected feature points to 3D coordinates from 2D image coordinates, also called residuals. Due to an embedded outlier detection scheme during feature matching and camera pose estimation, the difference in RMSE between the two experiments was marginal. Thus, one could conclude that the low-quality images had little influence on the reconstruction. However, this is not true. In Figure 10a,c, one can see that the result by all frames led to less reconstruction of the observed model. Without such a feature and outlier detection scheme, the point cloud and its resulting mesh would be noisier and suffer even more from false camera pose estimation and thus 3D reprojection biased by features of blurred frames. By reducing the number of images by the presented algorithm, the computation time for the reconstruction was significantly reduced. Hereby, we can see that the presented method decreased the computation time due to the reduced number of frames. The filtering of low-quality and redundant images had also led to a more considerable reconstruction.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Params** & **Values w.r.t. Whole Set of Frames** & **Values w.r.t. Selected Key Frames** \\  & **by the Proposed Method** \\ \hline RMSE & 1.041 & 0.979758 \\ Processing time & 1163.54 s & 169.835 s \\ Number of frames & 1535 & 474 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Feature importance resulted from the training of the random forest classifier. Features based on angular velocity have more influence on splitting leaves.  
Figure 6: Labeling result according to motion and quality requirements in accelerometer signal: (**a**) acceleration energy, (**b**) acceleration max norm, (**c**) acceleration min norm, (**d**) acceleration mean norm, and (**e**) acceleration variance.  
Figure 7: Labeling result according to motion and quality requirements in gyroscope signals: (**a**) angular velocity energy, (**b**) angular velocity max norm, (**c**) angular velocity min norm, (**d**) angular velocity mean norm, and (**e**) angular velocity variance.  
Figure 8: Classification report with respect to the test set. Performance of class _good_ underlies the performance of class _bad_.  
Figure 9: Random forest classification representation on \(S_{max}(a)\) and \(E_{S}(\omega)\). One can see a strong overlap between both classes, and the blue labels are significantly dominated by the red labels, **(a)** labeled representation of the training set, **(b)** more detailed representation focusing the blue labeled training data.