## 2 Materials and Methods
### Image Acquisition and Processing  
Structure from Motion (SfM) is a technique for generating a three-dimensional reconstruction from 2D images. Unlike traditional photogrammetry, which [PERSON] says [48] is \"the technique that aims to accurately study and define the shape, dimensions and position in the space of any object using essentially measurements made on one or more photographs,\" SfM uses computer vision algorithms to identify matching characteristics in a set of overlapping digital images, calculating the location and orientation of the camera. Based on these calculations, overlapping images can be used to reconstruct a structure using a 3D point cloud of the object, surface, or photo scene. It is possible to refine and densify the 3D structure generated by SfM using Multi-View Stereo (MVS), thus completing the SfM-MVS workflow, requiring photographs acquired by means of an optical sensor. For the reconstruction process to be successful, a large number of well-exposed photographs of the object, scene or surface of interest must be obtained at the stage of image acquisition, i.e., with a balance between shutter opening, shutter speed and ISO to correctly capture light. In addition, with sufficient resolution and overlap so that the computer vision algorithm can work effectively. The following are the steps of the method used for image acquisition and processing processes, which are schematically detailed in Figure 2.  
The technique of acquiring photographs from a UAV comes from a block of photographs formed from parallel flight lines, flown in a snake pattern at a stable altitude, with a constant overlap and a vertical camera angle (90\({}^{\circ}\)) [49]. However, the integration of oblique photographs can reduce the  
Figure 2: Workflow for the acquisition and processing under the SfM–MVS approach. systematic deformation resulting from inaccurate calculation to determine the internal geometry of the camera in the modern SfM-MVS photogrammetry [50; 51]. There is evidence that oblique photographs contribute to the integrity of the point cloud reconstruction [52]. Some recommendations for the acquisition of images are as follows: each point of the area of interest should appear in at least three images acquired at different positions; capture a static scene; use constant light, so that the color of the features does not vary; avoid overexposed or underexposed images; avoid blurry images, slow shutter speed or camera movement; and avoid transparent, reflective or homogeneous surfaces, as these surfaces cause difficulties in further processing [53]. An operational challenge of reconstruction through photographs captured from a UAV is to determine the optimal flight parameters required to achieve a better rebuild quality without excessively increasing flight time or processing. There are key aspects that determine the quality of the reconstruction of the image, mainly the solution of the reconstruction and the location accuracy of the points that rebuild the model. To find an adequate ratio between quality and efficiency, the UAV operator has a set of flight parameters to be adjusted; this includes the height and flight speed, image overlap and camera angle, among other technical sensor parameters such as ISO, shutter speed and shutter opening. The overlaps between images should be high enough to correctly produce 3D models, usually requiring front overlap of more than 75% (adjusted by varying the number of images per second) and the side overlap of more than 60% (critical variable adjusted in UAV flight path planning) [54]. The influence of lateral overlap on reconstruction is not well established, compared or tested despite its influence on flight efficiency [55]. For flight planning, it is important to consider the scale of the photographs, which will limit the detection and consequently the accuracy of the size of the objects in 3D reconstruction.