# Selection of Key Frames for 3D Reconstruction in Real Time
## 5 Conclusions  
We achieved a classifier that successfully omitted redundant frames and preserved frames with the required quality. However, it exhibited an unsatisfying performance with respect to ideal frames. Partly bad frames were unfortunately predicted as good ones. The main drawback was due to the acceleration values combined with slow motions, as one would record a video by preserving the quality of the individual frames. As we mentioned before, acceleration was unreliable as a metric during slow motion. Many frames of different labels refer to the same value of features, which made it ambiguous. Thus, we can say that when capturing high-quality frames, acceleration is not suitable to select them. Additionally, determining blurry frames was only possible by investigating a threshold. This depended strongly on the environment and brightness conditions seen in the frames. There was no ground truth indicating a proper bound, which made it impossible to evaluate new recordings based on the predefined threshold that was used for labeling. Due to the characteristics of acceleration, it was not reasonable to increase the threshold for optical flow. It would increase the necessary magnitude of acceleration,  
Figure 10: Resulting mesh of a 3D reconstruction of images by the open-source software Meshroom. (**a**,**c**) represent the result of the reconstruction due to the whole set of captured frames. On the other side, (**b**,**c**) represent the result of the reconstruction based on selected key frames by the proposed method. (**a**) The front side of the resulting mesh due to non-filtered frames, (**b**) the front side of the resulting mesh due to selected key frames that were determined by the proposed method, (**c**) the back side of the resulting mesh due to non-filtered frames, and (**d**) the back side of the resulting mesh due to filtered frames that were determined by the proposed method. which exhibits frames during higher acceleration and thus a higher probability of blurry edges. On the other side, measured values of accelerated motions would fall below the threshold of constant motion. Research had already shown that certain motion patterns can be well-differentiated by IMU if the motions are very different from each other [20; 21]. In this study, a method was presented that uses an individual calibration procedure and is based on the composition of the inertial data. This results in limitations to the interaction of camera frames and IMU, which in our case combines several inertial data per frame, as well as a complex calibration that is not based on a global optimum but must be determined individually. The trained RF classifier was used to reduce the number of frames to a subset of frames with non-blurry edges. This can help to ease further processing and omit redundant frames as well as blurry frames simultaneously. Using 3D reconstruction with Meshroom, we were able to show that our method led to an improved result for frames that are characterized by redundancy and motion blur. The insight here was a significant improvement achieved by decreasing the runtime of the calculation as well as by increasing the quality of the reconstructed model. For future work, one could determine the overlap between consecutive selected frames. Thus, the selection of the key frames is made by IMU and would be evaluated due to a certain overlap. One could define some threshold to require a minimum and maximum overlap. This would decrease the amount of similar successive frames despite motion. Such frames would differ significantly. Conceptualization, A.K. and C.M. ; methodology, A.K. and C.M. ; software, A.K. ; validation, C.M. ; formal analysis, A.K. ; investigation, A.K. ; resources, C.M. and A.R. ; data curation, A.K. ; writing--original draft preparation, A.K. ; writing--review and editing, A.K., C.M. and A.R. ; visualization, A.K. and C.M. ; supervision, C.M. and A.R. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable.