Learning Priors for Augmented Reality Tracking and Scene Understanding

Beschreibung

The great potential of Augmented Reality (AR) has started its realization in recent years. This recent surge came with an emergence of new challenges that upon successful completion will allow AR to reach technological maturity and become an essential part of everyday life. Even if pose tracking is a solved problem in controlled environments, challenges beyond that remain concerning geometric and semantic scene understanding. Dense mapping and semantic labeling of the environment is required for the creation of meaningful virtual content that is able to fully interact with the real world. When seen at a system level, scalability in the sense of device accessibility and content generation is the current main challenge for AR. In this thesis we provide novel solutions to several current challenges of AR concerning tracking, mapping, and applications. We base these solutions on generating prior knowledge using machine learning techniques. Deep Learning has already superseded traditional computer vision in areas such as classification but is challenged in 3D estimation problems. In this work we advocate a combination of initial hypotheses or prior estimates provided by Deep Learning with traditional computer vision to achieve refined and robust results.

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Jason Rambach

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Augmented Reality Deep Learning Computer Vision

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