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We present an application of synthetic datasets to a pose estimation problem called “Microwave Dish Mensuration”. Dish mensuration is the task of determining a microwave dish pointing angle from photogrammetry. Pose estimation presents a difficult case for machine learning, as it is onerous to collect a measured dataset capturing all possible configurations of an object or collection of objects; however, the ease of generating synthetic data may make the pose estimation problem tractable. Dish Mensuration has an additional benefit of having a well-known geometric invariance: a circular outline of a microwave dish, when rotated in 3D space, projects to an ellipse, and from the parameters of the ellipse, the 3D rotation relative to the sensor can be inferred. It is hoped that this geometric invariance will help the synthetic training regime generalize to measured data, and moreover, present a path forward to generalized models trained on synthetic datasets. For this research, we generated a dataset of 86,400 images of 5 different Microwave Dish models taken at 6 different times of day, generating both rendered image chips and component masks, facilitating pose estimation. We discuss the methods for generating the synthetic dataset, difficulties associated with generating sufficient variance, and a method for performing dish mensuration with a Deep Learning regression model. We conclude by addressing next steps and ways to further generalize into more pose estimation problems.
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