# Three-Dimensional Human Head Reconstruction Using Smartphone-Based Close-Range Video Photogrammetry
## 3 Results and Discussion
The details of data collection are presented in Section 2.5. A summary of the common statistics about processed experimental data is presented in Table 6. Sparse point clouds of reconstructed heads by using all reconstruction pipelines discussed in the article are presented in supplementary Figure S1. Comparative results of _Pipeline_ 1 and 2 reveal the influence of image background elimination on 3D head reconstruction. The evaluation of _Pipeline_ 3 shows the cumulative influence of an additional minor change--the selection of the best quality frames from several consecutive frames. The results of _Pipeline_ 4 reveal a larger influence of selection of the highest quality frames from the full set of frames. The construction of _Pipeline_ 4 required the selection of a combination of the feature sources (layer of CNN) and dimensionality-reduction (DR) technique used to order images by similarity. The latter comparison is performed in a separate experiment. _Pipeline_ 4 uses a more universal method to select images of the highest quality. If we have ordered images (i.e., frames with known poses in space), we could simply select the best image from the group of the closest images--this approach is implemented in _Pipeline_ 3. If the image pose is unknown, we first have to predict some probable relative pose, which can be done by ordering images according to similarity. For the image similarity assessment, we used features from CNN that were used to detect the head. The extracted feature vectors were used as descriptors of the image patch that holds the head. For image embedding in 2D or 3D (needed for frame selection method), dimensionality reduction is required, as image descriptors are multidimensional vectors. Dimensionality techniques were compared in combination with feature type (source layer of CNN). The results of the discussed methods for image ordering potency by similarity are presented in Table 3 (embedding in 2D case) and Table 4 (embedding in 3D case). The results in the tables represent the portion of correctly predicted images being the most similar to the reference image. A score of 100 would show that the method correctly predicts the full image group that is closest to the reference image when the reference closeness is calculated from the known image poses. The best performing combination of feature type and dimensionality-reduction technique is taking features from the 14 th convolutional layer _Conv 6 (fc6)_ and the t-SNE DR technique. This is valid in both 2D and 3D cases and in both head detection cases--single pass and a double pass. Comparing feature types and DR techniques separately, the findings are the same--features from the 14 th convolutional layer and t-SNE DR technique perform the best. From the DR techniques comparison, the second-best result is performing no dimensionality reduction. The second best feature source depends on the head detection strategy (one pass or two passes). Comparing the head detection strategies, the results show that the two-pass strategy helps increase the usefulness of features from further convolutional layers (starting from 17 th). The receptive fields of units from these layers are larger, the feature layers themselves are smaller, so the variations of head positioning within the receptive field are corrected more by head detection than selecting a feature vector from a suitable (row, column) location. If averaged over all feature layers, the two-pass strategy systematically increases the scores. Image embedding in 3D provides slightly better scores than that in 2D. The results of the main comparative evaluation of the general-purpose 3D reconstruction (as a baseline, _Pipeline_ 1), and its modifications (_Pipeline_ 2-4) we propose, are presented in Table 5. There are two evaluation cases: Evaluation Case 1 measures 3D head reconstruction residual errors in the entire region of the head, and in Evaluation Case 2 residuals are calculated only in the facial region of the head.