# Smartphone-Based Photogrammetry for the 3D Modeling of a Geomorphological Structure
## 3 D Model Generation: Methodology Assessment in a Controlled Environment
### Data Processing  
In order to evaluate the effectiveness of the smartphone-based methodology for the 3D reconstruction of geomorphological objects, the frames extracted from the video acquisition were processed and aligned through different software packages. To this purpose, two of the most popular pieces of software for image data processing were selected: the commercial solution Agisoft Metashape Professional (AMP) [42] and the open source VisualSfM (VSFM) [43]. Moreover, in order to evaluate the possibility of generating a point cloud in almost real-time and on the acquisition site, an application running on Android, SCANN3D, was tested. SCANN3D locally performs the 3D reconstruction, not on the cloud, in quite a rapid way, working entirely offline. The main benefit of the application is the real-time guidance during the image capturing phase, tracking multiple points in the viewpoints, which go from red to green when the camera has been moved sufficiently, ensuring a good image overlap. However, this application does not lock the focus and exposure after acquiring the first image, and this aspect can negatively affect the 3D reconstruction. In order to obtain a result comparable to that of the other software, it was decided to import and process the same image dataset. These pieces of software implement different algorithms deriving from the Computer Vision (CV) for image processing and matching, allowing, almost automatically, for the easy elaboration of a large number of images. These image-based reconstruction methods are generally based on the Structure from Motion (SfM) [44] approach and the Dense Image Matching (DIM) or Multi View Stereo (MVS) algorithms [45] and are characterized by five main steps, which require, as their input data, only the acquired images:  
* Feature extraction and identification of the matching points between images through different algorithms, such as the Scale Invariant Feature Transform (SIFT) algorithm [46], or its modified version [47];
* Outliers filtering, exploiting the Random Sample Consensus (RANSAC) algorithm [48], and a robust estimation of the acquisition geometry through linear models that impose geometric constraints;
* Estimate of the 3D coordinates of the object, the parameters of the internal and external orientation and the relative statistical information concerning the accuracy of the calculation, through the Bundle-Adjustment technique [49];  
Figure 4: The camera’s selected geometry configuration for data acquisition. Dense surface reconstruction of the point cloud identified for the orientation, exploiting several algorithms, including the Exact, Smooth, Height-field and Fast methods, for the object surface description and its main discontinuities [45];
* Dense point cloud interpolation and texturing in order to obtain a photorealistic visualization. The used pieces of software differ in terms of the algorithms involved in the image processing and 3D reconstruction pipeline; however, they all lead to the generation of the dense point cloud and, except for VS, to the production of a textured surface model. The georeferencing of the model can be performed during the image processing phase by importing, together with the images, the EXIF files containing the cameras acquisition points; otherwise, it is possible to manually identify some common points between the images and to assign them their coordinates in the chosen reference system (e.g., topographically measured markers). A first test was conducted using the advanced image-based software package AMP, which today is considered one of the best solutions for the generation of an accurate and complete dense point cloud in a very simple way. However, AMP can be defined as a \"black box\" since the implemented algorithms are not described in a deeper way. Subsequently, we tested the performance of the open source software vs. the integrated algorithm CMVS/PMVS [50].