## 2 Materials and Methods
### Deformation Analysis Using Point Clouds  
The emphasis in classic deformation analysis is on the discrete points which represent the object of interest. Deformation analysis compares the coordinates of the points, calculated from measurements in multiple epochs, to determine the possible displacements of the object with an appropriate statistical test of significance. Only a statistically verified result of a measurement may be used for further processing or interpretation. This is valid, in particular, for reliable alarm systems for the prevention of human and material damage [17]. The goal of the RGB+D approach is to bypass the drawbacks of laser scanner measurement -point clouds by enhancing the data with corresponding RGB data, preferably captured with the same instrument, such as an MS, to identify corresponding (discrete) points between subsequent measuring epochs [13]. The resulting data can then be integrated into a rigorous geodetic deformation analysis with a test of significance [15]. Matching Algorithms for RGB+D Data in Displacement Monitoring  
The biggest challenge of the RGB+D method used for displacement monitoring is how to identify corresponding (discrete) points between subsequent measuring epochs. Numerous matching algorithms have been developed for that purpose. The concept of feature detection and description refers to the process of identifying points in an image (interest points) that can be used to describe the image's contents, such as edges, corners, ridges, and blobs [21]. The most prominent and promising feature detection algorithms, applicable to RGB+D images in deformation analysis, are:  
* SIFT--Scale Invariant Feature Transformation [22];
* SURF--Speed Up Robust Features [23];
* BRISK--Binary Robust Invariant Scalable Keypoints [24];
* ORB--Oriented FAST and Rotated BRIEF [25]
* KAZE--Named after a Japanese word that means wind [26]. All the algorithms mentioned are scale invariant, which enables them to match image features despite the scale change of an object between images (movement). [PERSON] developed SIFT as a novel approach for image matching and feature detection. The basis of the algorithm is the transformation of image data into scale-invariant coordinates relative to the local features. An important aspect of the SIFT approach is that it generates a large number of features that densely cover the image over the full range of scales and locations [22]. SURF approximates, or even outperforms, previously proposed image matching algorithms with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much more quickly [23]. BRISK is proposed as an alternative image matching algorithm to SIFT and SURF, matching them in quality and with an emphasis on much less computation time [24]. Because of the binary nature of the algorithms descriptors, image features can be matched very efficiently, which makes the algorithm applicable for real-time use. ORB is a fast binary descriptor based on BRIEF (Binary robust independent elementary features [27]) which is rotation invariant and resistant to noise. The contribution of ORB is the addition of a fast and accurate orientation component to FAST (features from accelerated segment test [28]), the efficient computation of oriented BRIEF features, analysis of variance and correlation of oriented BRIEF features and a learning method for de-correlating BRIEF features under rotation invariance, leading to better performance in nearest-neighbor application [25]. KAZE is a multiscale 2D feature detection and description algorithm in nonlinear scale spaces. Feature detection and description in nonlinear scale spaces. In contrast to previous approaches that rely on the Gaussian scale space, this method is based on nonlinear scale spaces using efficient AOS techniques and variable conductance diffusion. Despite of moderate increase in computational cost, the results reveal a step forward in performance both in detection and description against previous state-of-the-art methods such as SURF or SIFT [26].