Generation and quality of as-built models influence their usage in subsequent applications such as progress monitoring,quality control, and deviation detection. The quality of any 3D reconstructed model heavily depends on the raw inputs and the post processing involved. While laser and LiDAR based scanning are widely prevalent, lower cost equipment and sensors are increasingly becoming adaptable for 3D reconstruction. However, quality of the raw data from the devices needs to be evaluated for effective utilisation. Low quality raw data would result in higher post processing time and would require computers with high computation capability. This would contradict the use of low cost devices. Therefore, it is essential to benchmark the quality of data and the ease of the scanning process for these devices. This study tests the feasibility of using IR based scanning tablets and stereo vision cameras to acquire data from a construction environment. Two different off-the-shelf devices: Project Tango and ZED camera are tested during this research for developing as-built models using 3D reconstruction. The devices are compared on the basis of the metrics such as preparation time for each scan, calibration of scanner and total scanning time for determining the ease of scanning process. This is assessed through an onsite study. To assess the quality of the scan, the scan accuracy is assessed by comparing it with a ground truth model. This study will also give insights into the internal factors that influence the quality of scanned data such as scanner sensor type and resolution accuracy. Also, influence of external factors such as scanning parameters, ambient lighting, and characteristics of object being scanned and angle/orientation of scanner with respect to the object are studied.