# Three-Dimensional Human Head Reconstruction Using Smartphone-Based Close-Range Video Photogrammetry
## 3 Results and Discussion
The results show that the lowest averages of residuals of all videos are provided by _Pipeline_ 4 (12 times smaller in Evaluation Case 1 and 7 times smaller in Evaluation Case 2, compared to _Pipeline_ 1). Comparing the submodifications a and b of the algorithms, in case b, when more features in the images are detected, the residuals are slightly lower. The influence of the semantic noise on the quality of the reconstructed head model is mainly reflected by the results  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline  & \multicolumn{6}{c}{**Variant of 3D Reconstruction Pipeline 1**} \\ \cline{2-9}
**Video** & **1 (Baseline)** & **2a** & **2b** & **3a** & **3b** & **4a** & **4b** \\ \hline \multicolumn{9}{c}{**Evaluation case 1 (full region of the head)**} \\ \hline
**  
of Evaluation Case 1 where residuals are calculated in the full area of the reference head. Evaluation Case 2 tells more about the model reconstruction errors (precision of the head reconstruction) that may be influenced by the quality of the images. Tendencies of the residual changes provided by different algorithms are consistent between these cases. If the scene we are trying to reconstruct was not static, the baseline reconstruction process might fail, as happened with the Video 10 case, because a moving background existed. Summarizing the data from Table 6, on average, _Pipeline_ 1-3 were compared using 412 frames and required over 12,000 image pairs to compare. _Pipeline_ 4 used on average 202 frames and required two times fewer image pairs to compare. The number of features to match mainly depended on the submodifications of _Pipelines_--in case b, more than twice the feature points in the images were detected. The baseline case used a large number of features because features in the background region were not discarded. The evaluation of the reduction of computational demand shows that the introduction of all proposed improvements compared to the baseline algorithm reduced the frame number needed to process by two times, reduced the number of image matches required to perform by six times, reduced the average number of feature points in images by 1.4 times, while the point count in the facial area of the head point cloud was reduced by only 1.2 times and provided the highest precision of the head reconstruction. The proposed photogrammetry algorithm improvements are highly adapted for head reconstruction. To extend the usage of the algorithm for the reconstruction of other objects, the head detector should be replaced with the detector of the target object. Object detectors trained to recognize multiple object classes may prove useful in this case. The proposed algorithms would be least adaptable for reconstruction applications beyond close-range photogrammetry. Background masking would not be applicable to aerial cases. There is still some room for additional improvements to the proposed algorithms. The initial feature selection step could be upgraded to be able to mask the background more precisely. The current head detector returns a bounding box that is used to select useful feature points, but the bounding box does not follow the head contours. Face detection and segmentation model, which provides a head segmentation mask, would allow the removal of more feature points that belong to the background region. Another upgradable operation is image subset selection using the regular grid after using the t-SNE dimensionality-reduction technique for ordering images by similarity. Due to the meaning of the distances in the case of t-SNE, the regular grid for partitioning the space in order to group similar images may not be the optimal approach. More sophisticated methods could better exploit the advantages of the t-SNE technique in finding the closest images. The results of experiments of the image ordering by similarity (Tables 3 and 4) show that the relative distances between images provided by t-SNE bear the largest amount of information about the real distances between the poses of the views.