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.
Object detection from high resolution images is increasingly used for many important application areas of defense and commercial sensing. However, object detection on high resolution images requires intensive computation, which makes it challenging to apply on resource-constrained platforms such as in edge-cloud deployments. In this work, we present a novel system for streamlined object detection on edge-cloud platforms. The system integrates multiple object detectors into an ensemble to improve detection accuracy and robustness. The subset of object detectors that is active in the ensemble can be changed dynamically to provide adaptively adjusted trade-offs among object detection accuracy, real-time performance, and energy consumption. Such adaptivity can be of great utility for resource-constrained deployment to edge-cloud environments, where the execution time and energy cost of full-accuracy processing may be excessive if utilized all of the time. To promote efficient and reliable implementation on resource-constrained devices, the proposed system design employs principles of signal processing oriented data ow modeling along with pipelining of data ow subsystems and integration on top of optimized, off-the-shelf software components for lower level processing. The effectiveness of the proposed object detection system is demonstrated through extensive experiments involving the Unmanned Aerial Vehicle Benchmark and KITTI Vision Benchmark Suite. While the proposed system is developed for the specific problem of object detection, we envision that the underlying design methodology, which integrates adaptive ensemble processing with data ow modeling and optimized lower level libraries, is applicable to a wide range of applications in defense and commercial sensing.
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