Smartphone‐based close‐range photogrammetric assessment of spherical objects

Abstract

Smartphones have widened the possibilities for low-cost close-range image acquisition for three-dimensional (3D) modelling. They allow the rapid acquisition of large amounts of data for a wide range of applications. However, the accuracy of the models and the automation possibilities depend on the image acquisition conditions and application requirements. In this study, the accuracy and reliability of the derived photogrammetric 3D models are evaluated on a spherical set-up for close-range applications (c.30 cm). Different numbers of images, network configurations, targets, devices and camera calibration methodologies are tested and evaluated. Results show that for this close-range application high accuracy (0Á2 mm) and reliability can be achieved. The number of images did not significantly affect the accuracy but was vital for tie-point detection and image orientation. The use of artificial targets was found to be the key factor in increasing the final accuracy. In contrast, the image calibration strategy and the characteristics of the imaging device did not have a great impact on the results.Smartphone video (more specifically slow-motion video) is a useful tool for acquiring large numbers of images, suitable even for fast moving objects. These images can be used for the creation of 3D models of moving objects (Barbero-Garc ıa et al., 2017). With an image acquisition speed of 240 frames per second (fps) of many smartphones (still far below the ultra-high-speed cameras that reach up to 2000 fps), the computational cost is the main limitation given the number of images to be used for 3D modelling.Despite their advantages, smartphone cameras present high internal instability that hampers their correct calibration. This problem is common to all non-metric digital cameras (Fraser, 2013), but is especially exacerbated when working with smartphones. The radiometric accuracy of smartphone cameras is lower than that of single-lens reflex (SLR) cameras but, despite their limitations, studies have concluded that these cameras can be used for photogrammetric tasks with a required accuracy of 1:10 000 (Akca and Gruen, 2009).The development of useful tools, which could allow non-expert users to obtain accurate 3D models for different purposes, requires a high degree of automation (Remondino et al., 2014). However, most of the available automatic low-cost solutions provide low repeatability and reliability (Remondino et al., 2012). The development of fully automatic and reliable solutions for specific applications requires an extensive knowledge of the factors affecting the quality of the 3D models created using smartphones or other similar imaging devices, such as tablets. The most important factors include the determination of the ideal geometric network, the selection of the best video frames and their optimal number, as well as the accuracy requirements for camera calibration. These parameters can vary greatly depending on the characteristics and limitations of the image acquisition process for a specific application (suc...

3D visualisation, communities and the production of significance

“…Identical points in consecutive images are identified allowing relative camera positions in 3D space to be inferred and from this the 3D location of multiple points on a target's surface can then be calculated. In the last few years, photogrammetric software has advanced to such a degree that manual intervention in image alignment is rarely required, and the quality of the resulting datasets and models rivals that of both structured light and laser scanning devices (for example see Barbero-García et al 2018). At the same time, the fact that consumer-grade cameras and cheap (even free) software can produce such impressive results has meant that the technical and financial barriers to use for private individuals and community groups have significantly diminished (Jeffrey et al 2015;Haukaas and Hodgetts 2016).…”

Smartphone Telemedicine: A Novel Workflow for Creating Prosthetic Sockets Using Semi-automated Photogrammetry

“…Finally, the algorithm generates a dense point cloud of the object from the matched and calculated feature points; a 3D mesh can be easily rendered from this point cloud data [10]. With the correct inputs, photogrammetric 3D models generated from smartphones are capable of producing sub-millimeter accuracy and precision [11].…”

“…One challenge of utilizing photogrammetry is that photographs (inputs) need to be of sufficient quality and with a large degree of overlap in order to successfully render 3D models (outputs). Unfortunately, existing low-cost solutions for automation are unreliable and unrepeatable [11]. Being able to consistently and quickly collect this data was a major challenge, so we determined it was essential to build a new tool to help automate the process.…”

“…Implementation of this function required our Android smartphones to have at least Android Version 5.0 in order to utilize Camera2 API. Barbero-Garcia et al [11] found that spherical objects could be successfully rendered with as few as 26 images. The application collects a minimum data set of 108 photos which is sufficient for rendering models.…”

“…2) Application Benchmark: Previous work by Barbero-Garcia et al [11] showed that smartphone photogrammetric methods can achieve sub-millimeter accuracy and precision under ideal conditions. However, some of these conditions are not possible outside of a laboratory setting.…”