## 1 Introduction
Most of the studies that evaluated CRP generally assessed its accuracy in extracting geometrical measurements. For instance, in the area of forestry, several studies [23; 24; 25; 26] evaluated the accuracy of CRP in estimating tree attributes (e.g., tree radius, circumference, and height) extracted from point clouds generated from digital compact camera images. Similarly, in the area of engineering, some studies assessed CRP according to its accuracy in estimating models' volumes [22; 27; 28] and deformation monitoring [29; 30; 31]. Otherstudies [32; 33; 34] in the area of cultural heritage evaluated CRP based on its quality for modeling and documenting architectural and archaeological structures. Only a few studies aimed to investigate smartphone's photogrammetric accuracy in general. For example, one study [27] evaluated CRP in estimating ground pile volume based on video frames captured by a digital camera and a smartphone. Their accuracy comparison was based only on the volumetric error. Another study [35] evaluated smartphones based on their potential and accuracy in modeling geomorphological structures. Another study [36] investigated the geometric accuracy of pre-calibrated smartphones' cameras for which their internal parameters were determined. Even though pre-calibrated cameras in general result in more accurate results, calibrating a digital compact camera or a smartphone is a time-consuming step that requires a precise procedure and needs to be conducted for each camera intended to be used. This study evaluated the potential of smartphones as an on-site data collection tool for data modeling and geometrical measurements extraction in comparison with a digital compact camera. The photogrammetric outputs (i.e., sparse cloud, dense cloud, polygonal, and textured models) of both cameras were compared based on their resulting parameters, processing time, and memory usage. Additionally, they were assessed and compared based on various quality criteria (e.g., sparse cloud noise, cloud density, points color, and texture representation). The accuracy of the 3D reconstruction for both data sets was examined based on the distortion parameters, RMS reprojection errors (i.e., image residuals), projections percentage, and tie multiplicity of the reconstructed points. These assessment criteria were selected based on previous studies [33; 37; 38; 39] assessing digital photogrammetry and according to some photogrammetric software guides [40; 41] in which various quality criteria and processing parameters were identified to impact the overall quality and reliability of the 3D reconstruction process and the different resulting outputs (e.g., tie points, dense clouds, and textured models). The study relied on the self-calibration approach conducted within the bundle adjustment algorithm based on the provided Exif data. Therefore, both cameras were not pre-calibrated, aiming to eliminate the camera calibration procedure required before capturing images; thus, any digital camera or smartphone can be utilized directly. Finally, both cameras were compared according to the estimation accuracy of the geometrical measurements (i.e., surface area and volume) extracted from the final textured 3D models of their data sets.