Given a set of images acquired from known viewpoints, we describe a method for synthesizing the image which would be seen from a new viewpoint. In contrast to existing techniques, which explicitly reconstruct the 3D geometry of the scene, we transform the problem to the reconstruction of colour rather than depth. This retains the bene\ufb01ts of geometric constraints, but projects out the ambiguities in depth estimation which occur in textureless regions. On the other hand, regularization is still needed in order to generate high-quality images. The paper\u2019s second contribution is to constrain the generated views to lie in the space of images whose texture statistics are those of the input images. This amounts to a image-based prior on the reconstruction which regularizes the solution, yielding realistic synthetic views. Examples are given of new view generation for cameras interpolated between the acquisition viewpoints\u2014which enables synthetic steadicam stabilization of a sequence with a high level of realism.<\/p>\n","protected":false},"excerpt":{"rendered":"
Given a set of images acquired from known viewpoints, we describe a method for synthesizing the image which would be seen from a new viewpoint. In contrast to existing techniques, which explicitly reconstruct the 3D geometry of the scene, we transform the problem to the reconstruction of colour rather than depth. 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