remote sensing Article Simulated Imagery Rendering Workflow for UAS-Based Photogrammetric 3D Reconstruction Accuracy Assessments Richard K. Slocum * and Christopher E. Parrish School of Civil and Construction Engineering, Oregon State University, 101 Kearney Hall, 1491 SW Campus Way, Corvallis, OR 97331, USA;
[email protected] * Correspondence:
[email protected]; Tel.: +1-703-973-1983 Academic Editors: Gonzalo Pajares Martinsanz, Xiaofeng Li and Prasad S. Thenkabail Received: 13 March 2017; Accepted: 19 April 2017; Published: 22 April 2017 Abstract: Structure from motion (SfM) and MultiView Stereo (MVS) algorithms are increasingly being applied to imagery from unmanned aircraft systems (UAS) to generate point cloud data for various surveying and mapping applications. To date, the options for assessing the spatial accuracy of the SfM-MVS point clouds have primarily been limited to empirical accuracy assessments, which involve comparisons against reference data sets, which are both independent and of higher accuracy than the data they are being used to test. The acquisition of these reference data sets can be expensive, time consuming, and logistically challenging. Furthermore, these experiments are also almost always unable to be perfectly replicated and can contain numerous confounding variables, such as sun angle, cloud cover, wind, movement of objects in the scene, and camera thermal noise, to name a few. The combination of these factors leads to a situation in which robust, repeatable experiments are cost prohibitive, and the experiment results are frequently site-specific and condition-specific. Here, we present a workflow to render computer generated imagery using a virtual environment which can mimic the independent variables that would be experienced in a real-world UAS imagery acquisition scenario.