Simulated Imagery Rendering Workflow for UAS-Based Photogrammetric 3D Reconstruction Accuracy Assessments

Abstract

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.

Evaluation of Unmanned Aircraft Systems for flood risk management : results of terrain and structure assessments

“…The Agisoft SfM processing quality level also contributed to improved results for both resolving the block corners and improving the RMSD. Similar results were found in Slocum and Parrish (2017) who simulated SfM workflows to assess reconstruction accuracy (i.e., the accuracy defined by tie-point matching and stereo reconstruction equations [not GCPs]).…”

Unsupervised Clustering of Multi-Perspective 3D Point Cloud Data in Marshes: A Case Study

“…SfM derives three-dimensional structure from two-dimensional image sequences through movement of the camera thereby providing different perspective views of the scene. The SfM image processing workflow is summarized as follows [19,20,30]:…”

“…In contrast to TLS, SfM implemented with an UAS (UAS-SFM) provides a nadir aerial perspective, which is more beneficial for regularized sampling of land cover and the underlying ground surface. However, SfM is a photogrammetric method that can be susceptible to false parallax stemming from dynamic surfaces, such as water movement or vegetation blowing in the wind, and poor feature matching due to low surface texture resulting in noisy or sparse point clouds over certain terrains [19][20][21]. Furthermore, UAS-SfM is limited in its ability to measure below canopy compared to active ranging techniques like airborne lidar or TLS, particularly when compared to lidar systems that employ multiple return echo detection.…”

Evaluation of the Volume Measurement Optical Method Suitability for Determining the Relative Compaction of Soils

“…Work of Slocum and Parrish [10] is an interesting example of verifying the accuracy of the method. The measured object was a computer model generated in a graphical environment.…”