## 2 Related Work  
The appearance of low-cost RGBD cameras on the market (e.g., Kinect) has contributed to a growing interest in the subject of 4D data analysis by researchers worldwide [1; 2; 3; 8; 9; 10; 11; 12; 13; 14]. The data obtained by such sensors are heavily noisy, but their low price and additional information about the depth correlated with the RGB image has created new opportunities. The authors of Reference [3] used three such sensors, together with pressure sensors placed in shoes, for pose estimation and registration of the triangle mesh. [PERSON] et al. [13] presented a method for pose estimation based on scans of a human body, using two opposite RGBD cameras and a pre-defined skeleton model. Once the skeleton base point is initialized with Principal Component Analysis (PCA), the individual scan parts are iteratively segmented and fitted based on Expectation Maximization (EM). In the skeleton model used, the geometric relationships between individual skeleton nodes are strictly defined. Some works also focused on hand movement tracking [9; 15; 16]. [PERSON] and [PERSON] [15] proposed a joint optimization method through energy minimization to track motion of hand in contact with a cloth surface. They track both deformations of the object and pose of the hand interacting with it, based on data from a Kinect2 sensor. One must also note the research in robotical environment geometry discovery [17; 18; 19; 20]. One of the best-known groups of methods regarding this field is called Structure from Motion (SfM) [21; 22], where geometry of the environment is computed from a series 2D images taken from different viewpoints. To this group belongs the algorithm developed by [PERSON] and [PERSON] [17] which focuses on the reconstruction of a surgical environment envisioned by using an endoscopic camera. The authors incorporated Unscented Kalman Filter, along with data from Inertial Measurement Unit (IMU), to achieve deformable Structure from Motion. A work by [PERSON] et al. [18] solves a rigid SfM problem by estimating the smooth time-trajectory of a camera moving around an object. Introduction of a parametrization in the Discrete Cosine Transform, along with the assumption of smooth camera trajectory, enabled the researchers to perform a non-rigid SfM in the presence of occlusions. To date, few works have focused on high-resolution data, due to high cost of obtaining such a system. One of the first collections of this type--which is available for a fee--is the CAESAR dataset(Civilian American and European Surface Anthropometry Resource Project) [23], containing about 4400 scans of different people. The pioneering work in the analysis of high-resolution 3D scans includes the SCAPE method (Shape Completion and Animation of People) [24], which is based on the model of body shape deformation as a function of the basic body shape of a given person and their pose at a specific moment. The authors presented the use of their method for the completion of unidirectional scans, as well as for the generation of mesh deformations in a sequence, based on a static scan of a person and a sequence of marker motions obtained by using the Motion Capture system. Another dataset of scans acquired with the use of a 3 DMD scanner has been made publicly available, under the name Dynamic FAUST (DFAUST) [25], which has provided important motivation for research development in the field of high-resolution 4D scan analysis techniques [23; 25; 26; 27; 28; 29; 30; 31; 32; 33]. This dataset consists of thousands of 3D scans of various people in different poses. The authors of the dataset developed it, using the Skinned Multi-Person Linear (SMPL) model [34], which is a trained model of various body shapes and their deformations with pose changes. Next, the authors used this model for pose and shape estimation from 2D images through the incorporation of a Convolutional Neural Network (CNN) [27; 28].