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VRST
1997
ACM

Image-based view synthesis by combining trilinear tensors and learning techniques

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Image-based view synthesis by combining trilinear tensors and learning techniques
We present a new method for rendering novel images of flexible 3D objects from a small number of example images in correspondence. The strength of the method is the ability to synthesize images whose viewing position is significantly far away from the viewing cone of the example images (“view extrapolation”), yet without ever modeling the 3D structure of the scene. The method relies on synthesizing a chain of “trilinear tensors” that governs the warping function from the example images to the novel image, together with a multi-dimensional interpolation function that synthesizes the non-rigidmotions of the viewed object from the virtual camera position. We show that two closely spaced example images alone are sufficient in practice to synthesize a significant viewing cone, thus demonstrating the ability of representing an object by a relatively small number of model images — for the purpose of cheap and fast viewers that can run on standard hardware.
Shai Avidan, Theodoros Evgeniou, Amnon Shashua, To
Added 07 Aug 2010
Updated 07 Aug 2010
Type Conference
Year 1997
Where VRST
Authors Shai Avidan, Theodoros Evgeniou, Amnon Shashua, Tomaso Poggio
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