2018
DOI: 10.48550/arxiv.1804.09004
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Cubes3D: Neural Network based Optical Flow in Omnidirectional Image Scenes

André Apitzsch,
Roman Seidel,
Gangolf Hirtz

Abstract: Optical flow estimation with convolutional neural networks (CNNs) has recently solved various tasks of computer vision successfully. In this paper we adapt a state-of-the-art approach for optical flow estimation to omnidirectional images. We investigate CNN architectures to determine high motion variations caused by the geometry of fish-eye images. Further we determine the qualitative influence of texture on the non-rigid object to the motion vectors. For evaluation of the results we create ground truth motion… Show more

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