# Three-Dimensional Human Head Reconstruction Using Smartphone-Based Close-Range Video Photogrammetry
## 2 Materials and Methods
### 3D Reconstruction Algorithms  
#### 2.1.1 Requirements for 3D Reconstruction Algorithm from Usability Viewpoint  
Shortcomings of the general-purpose 3D reconstruction algorithm in head modeling arise from the specifics of how the initial data (mostly it will be a selfie video) is collected and the kind of final reconstruction (model) we want to create. We aim to create a head model that is without semantic noise, i.e., the reconstructed scene contains only the head as an object and no other points that would belong to non-head objects. Such a model would not require any automatic or manual postprocessing, which would not necessarily be accurate and successful enough, but also, the model would be more appropriate for making measurements and for visualization purposes. Moreover, we want to create a model that has as few reconstruction errors as possible. Thus, we want the model to be high quality, i.e., having a low level of semantic noise and a low level of reconstruction errors.  
Semantic noise in the reconstructed scene will exist as everything will be reconstructed, not just the object of interest. The bad thing is that the noise will interfere with measurements or disturb visualization. It would be possible to edit or filter a point cloud, but this is a complicated task and does not guarantee a quality result. The other requirement for the data collection process in order for the general-purpose 3D reconstruction algorithm worked properly is that the scene must be static. However, if we are capturing our own head (most of the cases) or another person's head, it will not be possible to ensure that everything in the scene is fixed and does not move. Facial emotions during filming for 30-90 s could be controlled, but staying still so that there are no background changes is practically impossible. During reconstruction, a changing background would interfere with the reconstruction of the object, as richer textures in the background may result in a more accurate reconstruction of the object's environment, but not the object itself. A partial solution could be filming in the environment with a patternless, textureless background, but the user would need a background that spans almost entirely around them (such as a corner between walls of the same color) such a place may be hard to locate. Therefore, an easier solution would be to remove the background in the photos so that the background would not have influence.  
Another need for adjustment of the reconstruction algorithm is the specialization for working with videos. It is more convenient to film one's head than photograph it, especially if a person wants to image their head. Making selfie videos using a smartphone is more convenient than taking many selfie photos because, during the shooting, a user needs to keep the face as still as possible. Moreover, a user should not move the handheld camera too fast during filming in order to minimize image distortions, such as motion blur and rolling shutter. Slow camera movement during filming will create many similar frames, so it is not helpful to use all frames for the reconstruction. Due to the excessive number of repetitive images, the volume of calculations for the reconstruction will increase significantly, but the accuracy will practically not improve. It would be helpful to detect and remove from the reconstruction process frames that have highly redundant information. Among the many frames, there will also be low-quality ones, where the face is slightly outside the frame or affected by motion blur distortions due to a shaky hand. Such frames also need to be removed. Thus, the basic reconstruction algorithm has been supplemented with actions to remove unnecessary frames and, as a result, lower reconstruction errors.