KEYWORDS: Computer aided design, Solid modeling, Air force, Process modeling, Data modeling, Data analysis, Mathematical modeling, Analytic models, Sensors, Mathematics
The reconstruction of a watertight surface mesh from point clouds is a difficult problem. Constructing a watertight model from a polygonal mesh is just as difficult since there can be many issues in these models, such as intersecting surfaces and non-manifold geometry. We first describe a complete repair process for a single CAD object, resulting in a repaired static model. Next, we implement a novel workflow that can be used to repair local issues on almost every model, allowing one to use global repair methods on local areas of the model. This workflow can be applied to an assembly of CAD objects to retain articulations in the final repaired dynamic model. We introduce methods from Topological Data Analysis (TDA) to show that topological features can be used in the definition of robust mesh metrics, to characterize and determine quality of meshes, and to implement fully-automated watertight & repair of CAD meshes.
In Machine Learning (ML) based autonomous technology research (ATR), it is crucial to have large and reliable data sets to train deep learning-based classifiers and implement object detection methods. For air-to-ground ATR, the gold standard, obtained by limited and expensive controlled field collections, is measured data. However, carefully curated research data intended to test or isolate specific qualities of object detection (low-light, heavy shadow, cloud cover, obscurations, and other operational use cases) is still difficult to obtain. For advanced research problems, synthetic data generated in simulated environments meets both quantity and quality requirements. Most synthetic data is generated in a software simulated environment using various rendering techniques, limited by available computational resources. Among the many types of synthetic data is scale model data, generated by 3D printing and imaging the same 3D Computer-Aided Design (CAD) models at a reduced scale (1:285 or 1:125) on a turntable in controlled environmental conditions. We present a workflow for the rapid generation of ATR Training Data customized to isolate and identify features of interest in advanced research problems. Publicly accessible data is available upon request to lead author.
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