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. The resultant modular workflow utilizes Blender, an open source computer graphics software, for the generation of photogrammetrically-accurate imagery suitable for SfM processing, with explicit control of camera interior orientation, exterior orientation, texture of objects in the scene, placement of objects in the scene, and ground control point (GCP) accuracy. The challenges and steps required to validate the photogrammetric accuracy of computer generated imagery are discussed, and an example experiment assessing accuracy of an SfM derived point cloud from imagery rendered using a computer graphics workflow is presented. The proposed workflow shows promise as a useful tool for sensitivity analysis and SfM-MVS experimentation.
Highlights
Efficient acquisition of high-resolution, high-accuracy 3D point clouds has traditionally required either terrestrial, mobile, or airborne LiDAR
We propose an open-source computer graphics based workflow to alleviate the aforementioned issues with assessing the accuracy of point clouds generated from unmanned aircraft systems (UAS) imagery using structure from motion (SfM)-MultiView Stereo (MVS)
This study has demonstrated a new workflow leveraging the Blender Internal Render Engine, an open-source computer graphics render engine, to generate simulated UAS imagery data sets for rendered scenes, suitable for input into SfM-MVS software
Summary
Efficient acquisition of high-resolution, high-accuracy 3D point clouds has traditionally required either terrestrial, mobile, or airborne LiDAR. Advances in structure from motion (SfM) and MultiView Stereo (MVS) algorithms have enabled the generation of image-based point cloud products that are often reported to be comparable in density and accuracy to LiDAR data [1,2]. The 3D reconstruction methods used in most commercial software consist of an SfM algorithm first to solve for camera exterior and interior orientations, followed by an MVS algorithm to increase the density of the point cloud. Unordered photographs are input into the software, and a keypoint detection algorithm, such as scale invariant feature transform (SIFT) [6], is used to detect keypoints and keypoint correspondences between images using a keypoint descriptor. In addition to solving for camera interior and exterior orientation, the SfM algorithm generates a sparse point cloud. The coordinate system is arbitrary in translation and rotation and has inaccurate scale
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