Snap Angle Prediction for 360Â° Panoramas

2018-09-01 · ECCV 2018 9 · Bo Xiong, Kristen Grauman

360Â° panoramas are a rich medium, yet notoriously difficult to visualize in the 2D image plane. We explore how intelligent rotations of a spherical image may enable content-aware projection with fewer perceptible distortions. Whereas existing approaches assume the viewpoint is fixed, intuitively some viewing angles within the sphere preserve high-level objects better than others. To discover the relationship between these optimal emph{snap angles} and the spherical panorama's content, we develop a reinforcement learning approach for the cubemap projection model. Implemented as a deep recurrent neural network, our method selects a sequence of rotation actions and receives reward for avoiding cube boundaries that overlap with important foreground objects. Our results demonstrate the impact both qualitatively and quantitatively.

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Prediction reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

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