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Bridging The Simulation-To-Reality Gap: Adapting Simulation Environment For Object Recognition, Hardik Yogesh Sonetta

Electronic Theses and Dissertations

Rapid advancements in object recognition have created a huge demand for labeled datasets for the task of training, testing, and validation of different techniques. Due to the wide range of applications, object models in the datasets need to cover both variations in geometric features and diverse conditions in which sensory inputs are obtained. Also, the need to manually label the object models is cumbersome. As a result, it becomes difficult for researchers to gain access to adequate datasets for the development of new methods or algorithms. In comparison, computer simulation has been considered a cost-effective solution to generate simulated data …