Image-Based View Synthesis

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-rigid motions 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

Robust Recovery of Camera Rotation from Three Frames

Computing camera rotation from image sequences can serve many computer vision applications. One direct application is image stabilization, and when the camera rotation is known the computation of camera translation and 3D scene structure are much simplified. A new approach for recovering camera rotation is presented in this paper, which proves to be much more robust than existing methods by avoiding the computation of the epipole. Another benefit of the new approach is that it does not assume any specific scene structure. The rotation matrix of the camera is computed explicitly from three homography matrices, recovered using the trilinear tensor which describes the relations between the projections of a 3D point into three images. The entire computation is linear for small angles, and is therefore fast and stable. Iterating the linear computation can then be used to recover larger rotations as well. 1 Introduction Recovering camera rotation is one of the basic steps in many image seq..