## 1 Introduction  
The ability to provide accurate geometrical estimations (e.g., surface areas and materials volumes) is essential in the field of construction management since they are a key for generating accurate quantity take-off and cost estimation reports during tasks planning. They are also essential for progress tracking and forecasting during tasks' implementation. Additionally, accurate progress measurements can be used as input data for simulating and optimizing construction tasks. For instance, a crew working on an earthmoving operation can be optimized based on its measured actual performance, thus resulting in an optimum crew configuration with the least cost [1]. In most construction projects, conventional methods of estimation, which are time-consuming, cost-ineffective, and error-prone, continue to be used. On the other hand, the technology of digital photogrammetry, both terrestrial and aerial, was established to be a remarkable data collection approach for 3D data modeling and measurements extraction in various construction applications, such as building modeling and documentation [2; 3; 4], progress tracking [5; 6; 7; 8; 9], and measurement extractions [10; 11]. Although most of the research integrating photogrammetry with construction management is focused mainly on assessing and utilizing the aerial type using UAV platforms due to their higher efficiency to cover large areas [2; 6; 11], close-range photogrammetry can be suitable for scanning relatively smaller areas and tracking indoor tasks. Close range photogrammetry (CRP) processes ground-based overlapping images via a structure from motion (SFM) pipeline. The processing starts by implementing the scale invariant feature transform (SIFT) algorithm [12; 13; 14] or similar algorithms (e.g., SURF and ORB). SIFT detects images' features and matches those shared by overlapping images by comparing their descriptors. Through bundle block adjustment (BBA) [15; 16; 17; 18], the camera calibration parameters (i.e., translating, rotation, focal lengths, the optical center, and lens distortions) are estimated and optimized. After estimating the camera poses, the matched features get reconstructed as 3D points by triangulating them from all overlapping images, resulting in a sparse point cloud. The sparse cloud can be densified by generating and merging depth maps of the overlapping images [19; 20; 21]. From the densified cloud, a polygonal mesh can be generated using mesh triangulation algorithms (e.g., Poisson reconstruction). For providing the polygonal mesh with a photorealistic appearance, a colored texture map is generated. The accuracy and quality of these photogrammetric outputs depend significantly on the specification of the camera used for acquiring images. In most research, advanced compact cameras with high resolution and less distortion are used. Others used pre-calibrated cameras for which their calibration parameters were precisely determined before data capturing. For instance, one study [22] evaluated the accuracy of CRP as a measuring tool using a pre-calibrated digital compact camera. Nevertheless, it is important to aim for making CRP more accessible and feasible not only by utilizing inexpensive compact cameras but also by using smartphones directly without a pre-calibration procedure. Nowadays, smartphones are associated with cameras that are of sufficient potential to provide reasonably high-quality images. Smartphones are highly accessible compared with compact cameras and can be utilized for acquiring photogrammetric images by any worker on a construction site. Additionally, smartphones excel compact cameras in their ability to send the collected images on-site to any processing device via Wi-Fi or Bluetooth, thus providing near real-time measurements. Therefore, it is essential to present a study that fully assesses the quality and accuracy of the 3D models generated from smartphone data and compares their results with those of a compact camera.