## 1 Introduction
One way to obtain a personalized HRTF is through binaural acoustic measurements, where microphones are inserted into the subject's ear canal and a large number of sound sources are placed in a semicircle around the human head at different elevation angles, but maintaining the same radius. The subject is seated in an automated rotating chair and measurements are taken each time the chair is repositioned at a fixed azimuth [12]. Such measurements are difficult and available only for scientific purposes, requiring expensive equipment, an anechoic room, and expertise. Although there have been attempts to simplify this process to one that could be performed at home using only a single speaker and a head tracking system [13], or using a smartphone by moving it around the head, storing the phone's position in space, and recording the test signal played by the phone with microphones built into headphones [14]. Researchers are constantly looking for ways to easily obtain as close to an individual HRTF as possible without complicated measurement procedures. There are suggestions to select the most appropriate HRTF from the ones available by analyzing the anthropometric data of the subject whose personalized HRTF we are looking for and identifying the most similar object in the selected database [15; 16]. Another way to select the HRTF matcher is to classify the anthropometric data of the database objects into clusters, highlight the centers of these clusters, identify which cluster best fits the subject's ear, and select the central HRTF of that cluster as the subject's individual HRTF [17]. In addition to acoustic measurements and the most similar HRTF fitting methods, numerical calculations are also possible when trying to calculate the expected HRTF spectrum by analyzing how sound waves of different frequencies will reach the eardrum point geometrically from all possible directions. For such algorithms, it is necessary to provide an accurate 3D model of the head and ears. The mesh can be created in many ways--thehead can be laser scanned [18] or it can be reconstructed photogrammetrically [19; 20]. With an accurate 3D model, we can calculate a human-customized HRTF filter; researchers have developed tools for this task [21]. An HRTF filter can also be computed using deep neural networks (DNNs) with anthropometric measurements and ear images [22] or sound source directions [23] as input. DNN can also be used to detect an ear in an image and automatically label the points that define its shape [24; 25; 26; 27; 28; 29]. Image-based reconstruction and modeling of objects [30; 31; 32], scenes [33; 34; 35; 36; 37; 38], or processes [39; 40], is a widely available and relatively inexpensive technique for information acquisition [41; 42; 43]. One of the photogrammetric methods is the structure-from-motion (SfM) technique [44; 45; 46]. This provides a noninvasive, cost-effective means of creating three-dimensional models of objects and environments from sets of two-dimensional images [47; 48; 49; 50; 51]. The SfM approach works by first estimating camera positions and then progressively reconstructing the 3D coordinates of the points in the scene [52; 53; 54]. SfM is highly valuable in the sense that it allows for the reconstruction of 3D structures using images taken from hand-held cameras [55; 56; 57; 58; 59]. However, the accuracy of SfM is intrinsically linked to the quality of the input images and the precision of camera localization [60; 61; 62]. Deep neural networks have been used to implement SfM tasks that include feature detection, matching, and outlier rejection [63; 64; 65; 66; 67]. Deep learning can automatically learn feature representations and mapping functions from large volumes of data, which has been shown to improve the robustness and accuracy of SfM [68; 69; 70]. Deep learning has also been combined with traditional SfM techniques to create hybrid models, which can take advantage of the strengths of both approaches [71; 72].